EffectiveIndexApproximation.ipynb

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    "# 2D effective index approximation of 3D simulations\n",
    "\n",
    "Large-scale 3D FDTD simulations are often required for accurate results but are associated with significant time and costs. Therefore, the development of methods to approximate 3D simulations using 2D models presents notable advantages. For instance, during the initial phase of design exploration, employing 2D simulations can effectively narrow down the range of parameters, with 3D simulations subsequently refining and finalizing the design.\n",
    "\n",
    "This notebook introduces an efficient index approximation method that enables the approximation of 3D FDTD simulations through 2D simulations, substantially reducing computational resources and simulation times, potentially by orders of magnitude, while still retaining a reasonable level of accuracy. This method is particularly suitable for modeling integrated photonic devices that support waveguide modes. We first outline the process for calculating the effective permittivity for the 2D simulation using the variational method.\n",
    "\n",
    "Following this, we compare the results of 1D approximation of a 2D device, and a 2D approximation of a 3D device to validate the approximation method's effectiveness. The findings indicate that, when applied appropriately, the approximated simulation maintains an acceptable fidelity to the corresponding full simulation response.\n",
    "\n",
    "<a href=\"https://doi.org/10.1007/s11082-009-9349-3\"><img src=\"img/effective_index_approximation.png\" width=\"500\" alt=\"Schematic of 2D-ifying\" ></a>\n",
    "\n",
    "First we start by importing the necessary libraries for our simulations:"
   ]
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    "import gdstk\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import tidy3d as td\n",
    "import tidy3d.web as web\n",
    "from tidy3d.plugins.dispersion import AdvancedFastFitterParam, FastDispersionFitter"
   ]
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    "## Variational Effective Permittivity\n",
    "\n",
    "In order to match the material of our 2D structures with those of the corresponding 3D structures, we will calculate the <b>variational effective permittivity</b>, taken from `Manfred Hammer and Olena V. Ivanova, \"Effective index approximations of photonic crystal slabs: a 2-to-1-D assessment,\" Opt Quant Electron 41, 267–283 (2009)`[DOI:10.1007/s11082-009-9349-3](https://doi.org/10.1007/s11082-009-9349-3):<br>\n",
    "$$\n",
    "\\epsilon_{eff}(x,y)=n_{eff}(x_r,y_r)^2+\\frac{\\int_z[\\epsilon(x,y,z)-\\epsilon(x_r,y_r,z,\\omega)]|M_r(z,\\omega)|^2dz}{\\int_z|M_r(z,\\omega)|^2dz},\n",
    "$$<br>\n",
    "where $x_r,y_r$ are the coordinates of the reference point, $n_{eff}(x_r,y_r)$ is the effective index of the slab waveguide, given the permittivity profile in the z direction, at the reference point, $\\epsilon$ is the permittivity profile, and $M_{ref}(z,\\omega)$ is the mode profile in $z$ at the reference point. This formula is based on the assumption that the different vertical slab modes remain relatively uncoupled.<br><br>\n",
    "\n",
    "The reference profile in $z$ at point $(x_r,y_r)$ is what we choose to compare other $z$ profiles against. In our examples, we choose this point to include the location of our waveguide.<br><br>\n",
    "\n",
    "Note: This is only the effective permittivity for the TE mode. A separate TM mode formula exists, and the interested user should refer to the TM formula given in the paper. Also, in the paper, the extrusion dimension is x. In our examples, the extrusion dimension is z."
   ]
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  {
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    "import scipy.interpolate\n",
    "from tidy3d.plugins.mode.web import run as run_mode_solver\n",
    "\n",
    "\n",
    "def var_eps_eff(point, ref_point, sim, wavelength=1.55, inf=1000, min_n=1, remote=False):\n",
    "    \"\"\"\n",
    "    To calculate the vertical slab mode at 'ref_point', we will create a 2D slice of the given simulation at\n",
    "    'ref_point' that extends in the yz plane infinitely, ensuring that it captures the entire permittivity\n",
    "    profile. Then, to find the 1D mode profile, we use the Tidy3D ModeSolver on a plane at 'ref_point' that\n",
    "    extends infinitely in the xz plane. This results in the ModeSolver operating on the intersecting line\n",
    "    at 'ref_point' that covers its entire z profile. We then use this to solve for n_eff and M in the above\n",
    "    formula.\n",
    "\n",
    "    The min_n parameter is set to ensure that the calculated refractive index is at least 1. This\n",
    "    calculation can well end up with a value less than 1, as mentioned in the paper. To ensure that the\n",
    "    returned refractive index is physical, we set min_n to 1 by default.\n",
    "\n",
    "    There is also the option to use the remote mode solver if the user desires greater accuracy.\n",
    "    \"\"\"\n",
    "    freq = td.C_0 / wavelength\n",
    "\n",
    "    sim_2d_center = (\n",
    "        ref_point[0],\n",
    "        ref_point[1],\n",
    "        0,\n",
    "    )  # given a 3D sim, we update the center to create our 2D slice\n",
    "    sim_2d_size = (\n",
    "        0,\n",
    "        inf,\n",
    "        inf,\n",
    "    )  # we ensure the 2D span of the simulation in the yz plane captures everything\n",
    "\n",
    "    # now we create the 2D simulation, keeping the structures and updating the boundary conditions\n",
    "    sim_2d = sim.updated_copy(\n",
    "        center=sim_2d_center,\n",
    "        size=sim_2d_size,\n",
    "        sources=[],\n",
    "        monitors=[],\n",
    "        symmetry=(0, 0, 0),\n",
    "        boundary_spec=sim.boundary_spec.updated_copy(x=td.Boundary.periodic()),\n",
    "    )\n",
    "\n",
    "    # Now we solve for the mode at 'ref_point':\n",
    "    # We create the plane in xz that we'll use to examine the mode in z\n",
    "    mode_solver_plane = td.Box(center=sim_2d.center, size=(td.inf, 0, td.inf))\n",
    "    # Now we define the mode solver using this plane. We need only solve for one mode here, hence the ModeSpec\n",
    "    mode_solver = td.plugins.mode.ModeSolver(\n",
    "        simulation=sim_2d, plane=mode_solver_plane, mode_spec=td.ModeSpec(num_modes=1), freqs=[freq]\n",
    "    )\n",
    "\n",
    "    # Note that here the mode solving is done locally. For users desiring more accuracy, the remote mode\n",
    "    # solver should be used.\n",
    "    if remote:\n",
    "        mode_data_ref = run_mode_solver(mode_solver)\n",
    "    else:\n",
    "        mode_data_ref = mode_solver.solve()\n",
    "\n",
    "    # get n_eff from the solver\n",
    "    n_eff = mode_data_ref.n_eff.item()\n",
    "    if point == ref_point:\n",
    "        return n_eff**2  # if point is the reference point, the integral is 0\n",
    "\n",
    "    # get z permittivity profile at 'ref_point'\n",
    "    x, y = ref_point\n",
    "    eps_ref = sim.epsilon(\n",
    "        box=td.Box(center=(x, y, list(sim.center)[2]), size=(0, 0, td.inf)), freq=freq\n",
    "    )\n",
    "\n",
    "    # get z permittivity profile at 'point'\n",
    "    x, y = point\n",
    "    eps = sim.epsilon(\n",
    "        box=td.Box(center=(x, y, list(sim.center)[2]), size=(0, 0, td.inf)), freq=freq\n",
    "    )\n",
    "\n",
    "    eps_dif = np.squeeze(eps.values) - np.squeeze(eps_ref.values)\n",
    "\n",
    "    # get M at the same z coordinates as those of (eps - eps_ref) so we can integrate their product\n",
    "    z_coords = eps_ref.z.values\n",
    "    mode_profile = mode_data_ref.Ex\n",
    "    Mz2 = scipy.interpolate.interp1d(\n",
    "        x=mode_profile.z.values, y=np.abs(np.squeeze(mode_profile.values)) ** 2\n",
    "    )\n",
    "    m_values = Mz2(z_coords)\n",
    "\n",
    "    # calculate integrals\n",
    "    num, denom = np.trapz(y=eps_dif * m_values, x=z_coords), np.trapz(y=m_values, x=z_coords)\n",
    "\n",
    "    if n_eff**2 + num / denom < min_n:\n",
    "        return min_n\n",
    "    return n_eff**2 + num / denom"
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    "Here we create a function that uses the above function to create the approximated materials used in the 2D simulation. It takes as input the 3D simulation, the mode reference point, the point describing the stack that the user would like to approximate, and the simulation spectrum."
   ]
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    "def approximate_material(sim_3D, approx_point, ref_point, spectrum, min_n=1, plot=False):\n",
    "    eps = []\n",
    "    for wl in spectrum:  # at every [step]th wavelength, calculate the effective permittivity\n",
    "        eps.append(var_eps_eff(approx_point, ref_point, sim_3D, wavelength=wl, min_n=min_n))\n",
    "\n",
    "    # fit the materials with the FastDispersionFitter using the calculated effective permittivities\n",
    "    fitter = FastDispersionFitter(wvl_um=spectrum, n_data=np.sqrt(np.real(eps)))\n",
    "\n",
    "    # create the mediums using the material fit\n",
    "    medium, rms_error = fitter.fit()\n",
    "\n",
    "    if plot:\n",
    "        # plot the material fit\n",
    "        fig, ax = plt.subplots(1, 1, figsize=(3, 3))\n",
    "        fitter.plot(medium, ax=ax)\n",
    "        ax.set_title(\"Medium\")\n",
    "        plt.show()\n",
    "\n",
    "    return medium"
   ]
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   "id": "2361f1c4-6e45-4264-a41b-12c860d9d780",
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   "source": [
    "Here we create a function that, given a 3D simulation, creates a 2D copy with updated materials given by the user. This, like the previous function, is provided so Tidy3D users can easily 2D-ify their 3D simulations."
   ]
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    "def create_2D_sim(sim_3D, new_mediums):\n",
    "    # Redo the structures that were in our 3D simulation with our calculated material approximations\n",
    "    new_structures = []\n",
    "    for structure in sim_3D.structures:\n",
    "        new_structures.append(structure.updated_copy(medium=new_mediums[0]))\n",
    "\n",
    "    # Update the size to 3D\n",
    "    new_size = list(sim_3D.size)\n",
    "    new_size[2] = 0\n",
    "\n",
    "    # Update the symmetry to 2D\n",
    "    new_symmetry = list(sim_3D.symmetry)\n",
    "    if new_symmetry != [0, 0, 0]:\n",
    "        print(\n",
    "            \"Note: removed symmetry, as it is incompatible with the variational approximation method.\"\n",
    "        )\n",
    "    new_symmetry = (0, 0, 0)\n",
    "\n",
    "    # Create the 2D version of our 3D original\n",
    "    sim_2D = sim_3D.updated_copy(\n",
    "        size=new_size,\n",
    "        structures=new_structures,\n",
    "        boundary_spec=sim_3D.boundary_spec.updated_copy(z=td.Boundary.periodic()),\n",
    "        medium=new_mediums[1],\n",
    "        symmetry=new_symmetry,\n",
    "    )\n",
    "\n",
    "    return sim_2D"
   ]
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   "source": [
    "Since the method that calculates the variational effective permittivity creates a 2D simulation from the given 3D simulation, generally there can be some objects (and symmetries) not included in the 2D simulation, which will throw some warnings. Furthermore, since we're using the local mode solver, Tidy3D will also print a warning that the remote mode solver should be used for greater accuracy. Since we're okay with this, it will be cleaner to suppress the warnings."
   ]
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    "# Suppress warnings for some of the structures not being included in the 2D simulation\n",
    "td.config.logging_level = \"ERROR\""
   ]
  },
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   "source": [
    "The above four code cells are all that is needed to use this approximation for a 3D simulation given by the user."
   ]
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    "## Example 1: 2D Planar Waveguide Bragg Grating\n",
    "To see this in action, we will apply this to a 2-dimensional waveguide Bragg grating described in the source paper.<br>\n",
    "\n",
    "First we quickly build the 2D simulation:"
   ]
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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Define wavelength/frequency ranges\n",
    "ldas = np.linspace(0.4, 0.9, 101)\n",
    "lda0 = ldas[50]\n",
    "freqs = td.C_0 / ldas\n",
    "freq0 = td.C_0 / lda0\n",
    "\n",
    "# Refractive indices\n",
    "n_c = 1\n",
    "n_f = 2\n",
    "n_s = 1.45\n",
    "\n",
    "# Materials used\n",
    "air = td.Medium(permittivity=n_c**2)\n",
    "top_medium = td.Medium(permittivity=n_f**2)\n",
    "bottom_medium = td.Medium(permittivity=n_s**2)\n",
    "\n",
    "# Define geometries and their structures\n",
    "hole_length = 0.11\n",
    "hole_depth = 0.6\n",
    "hole_spacing = 0.21\n",
    "top_thickness = 0.2\n",
    "\n",
    "inf = 10\n",
    "\n",
    "holes_list = []\n",
    "for h in range(6):\n",
    "    holes_list.append(\n",
    "        td.Box(\n",
    "            center=(hole_depth + h * hole_spacing, 0, 0), size=(hole_length, inf, 2 * hole_depth)\n",
    "        )\n",
    "    )\n",
    "holes_geos = td.GeometryGroup(geometries=holes_list)\n",
    "holes = td.Structure(geometry=holes_geos, medium=air)\n",
    "\n",
    "top_geo = td.Box.from_bounds(rmin=(-inf, -inf, -top_thickness), rmax=(inf, inf, 0))\n",
    "bottom_geo = td.Box.from_bounds(rmin=(-inf, -inf, -inf), rmax=(inf, inf, 0))\n",
    "\n",
    "top = td.Structure(geometry=top_geo, medium=top_medium)\n",
    "bottom = td.Structure(geometry=bottom_geo, medium=bottom_medium)\n",
    "\n",
    "# The injection used in the paper is a plane wave source\n",
    "source = td.PlaneWave(\n",
    "    center=(hole_depth * 0.1, 0, -top_thickness / 2),\n",
    "    size=(0, inf, top_thickness),\n",
    "    source_time=td.GaussianPulse(freq0=freq0, fwidth=freqs[0] - freqs[-1]),\n",
    "    direction=\"+\",\n",
    ")\n",
    "\n",
    "# Add monitors\n",
    "reflection = td.FluxMonitor(\n",
    "    center=(hole_depth * 0.05, 0, 0), size=(0, inf, inf), name=\"R\", freqs=freqs, normal_dir=\"-\"\n",
    ")\n",
    "transmission = td.FluxMonitor(\n",
    "    center=(hole_depth + 7 * hole_spacing, 0, 0), size=(0, inf, inf), name=\"T\", freqs=freqs\n",
    ")\n",
    "\n",
    "# Define the simulation of adequate size\n",
    "sim_size_x = hole_depth * 1.1 + 7 * hole_spacing\n",
    "sim_2d = td.Simulation(\n",
    "    center=(sim_size_x / 2, 0, -hole_depth / 3),\n",
    "    size=(sim_size_x, 0, 4 * hole_depth),\n",
    "    medium=air,\n",
    "    structures=[bottom, top, holes],\n",
    "    sources=[source],\n",
    "    monitors=[reflection, transmission],\n",
    "    boundary_spec=td.BoundarySpec(\n",
    "        x=td.Boundary.pml(), y=td.Boundary.periodic(), z=td.Boundary.pml()\n",
    "    ),\n",
    "    grid_spec=td.GridSpec.auto(wavelength=lda0, min_steps_per_wvl=30),\n",
    "    run_time=150 / freq0,\n",
    ")\n",
    "\n",
    "# Visualize the simulation to check\n",
    "sim_2d.plot_eps(y=0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b4f374c-d13a-41d0-a585-cda121bfc785",
   "metadata": {},
   "source": [
    "Next we calculate the variational effective indices at different steps in our input wavelengths. We will then fit a dispersive material to this data using Tidy3D's FastDispersionFitter.<br><br>\n",
    "To speed things up, we will reduce the number of times we need to solve for the mode, so we will downsample the wavelength points. Here we calculate the effective permittivity at every fifteenth wavelength."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7971bed4-577f-4144-add6-2642c440de2f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3d550e5dec994cea9d5f0bf1d485a0f2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 300x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Downsample wavelengths to speed up the calculation\n",
    "spectrum = ldas[::15]\n",
    "waveguide_mat = approximate_material(sim_2d, (0.25, 0), (0.25, 0), spectrum, plot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5a059418-86f2-448c-bb4e-1cde25bb759f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f8fadeceda4f4119a7a5b67b8af3e8da",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hole_mat = approximate_material(sim_2d, (1, 0), (0.25, 0), spectrum)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "357cc006-a7f4-44a7-a878-5d48ef8a5786",
   "metadata": {},
   "source": [
    "We now create the 1D simulation with the calculated mediums:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a9478257-8f54-46b6-aef4-3d576be956eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Redo the structures that were in our 2D simulation with our calculated material approximations\n",
    "holes_list_1D = []\n",
    "for h in range(6):\n",
    "    holes_list_1D.append(\n",
    "        td.Box(center=(hole_depth + h * hole_spacing, 0, 0), size=(hole_length, inf, inf))\n",
    "    )\n",
    "holes_geos_1D = td.GeometryGroup(geometries=holes_list_1D)\n",
    "holes_1D = td.Structure(geometry=holes_geos_1D, medium=hole_mat)\n",
    "\n",
    "# Update the size to 1D\n",
    "new_size = list(sim_2d.size)\n",
    "new_size[2] = 0\n",
    "\n",
    "# Update the plane wave source for 1D\n",
    "source_1d = td.PlaneWave(\n",
    "    center=sim_2d.sources[0].center,\n",
    "    size=sim_2d.sources[0].size,\n",
    "    source_time=sim_2d.sources[0].source_time,\n",
    "    direction=sim_2d.sources[0].direction,\n",
    ")\n",
    "\n",
    "# Create the 1D version of our 2D original\n",
    "sim_1d = sim_2d.updated_copy(\n",
    "    size=new_size,\n",
    "    structures=[holes_1D],\n",
    "    sources=[source_1d],\n",
    "    boundary_spec=sim_2d.boundary_spec.updated_copy(z=td.Boundary.periodic()),\n",
    "    medium=waveguide_mat,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11e85207-1488-48f3-8d2e-fc6049e17865",
   "metadata": {},
   "source": [
    "Finally, we run both simulations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "fb80a1cd-8013-438f-9581-3617db296df0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'fdve-329f73f4-e53d-4f75-ace2-c6c16f717e99'</span> and task_type <span style=\"color: #008000; text-decoration-color: #008000\">'FDTD'</span>.  \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m13:30:09 EST\u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'var eps'\u001b[0m with task_id                                \n",
       "\u001b[2;36m             \u001b[0m\u001b[32m'fdve-329f73f4-e53d-4f75-ace2-c6c16f717e99'\u001b[0m and task_type \u001b[32m'FDTD'\u001b[0m.  \n"
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     "metadata": {},
     "output_type": "display_data"
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    {
     "data": {
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>View task using web UI at                                          \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-329f73f4-e53d-4f75-ace2-c6c16f717e99\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'https://tidy3d.simulation.cloud/workbench?taskId=fdve-329f73f4-e53</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-329f73f4-e53d-4f75-ace2-c6c16f717e99\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">d-4f75-ace2-c6c16f717e99'</span></a>.                                         \n",
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      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mView task using web UI at                                          \n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=361464;https://tidy3d.simulation.cloud/workbench?taskId=fdve-329f73f4-e53d-4f75-ace2-c6c16f717e99\u001b\\\u001b[32m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=633718;https://tidy3d.simulation.cloud/workbench?taskId=fdve-329f73f4-e53d-4f75-ace2-c6c16f717e99\u001b\\\u001b[32mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=361464;https://tidy3d.simulation.cloud/workbench?taskId=fdve-329f73f4-e53d-4f75-ace2-c6c16f717e99\u001b\\\u001b[32m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=509360;https://tidy3d.simulation.cloud/workbench?taskId=fdve-329f73f4-e53d-4f75-ace2-c6c16f717e99\u001b\\\u001b[32mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=361464;https://tidy3d.simulation.cloud/workbench?taskId=fdve-329f73f4-e53d-4f75-ace2-c6c16f717e99\u001b\\\u001b[32m-329f73f4-e53\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=361464;https://tidy3d.simulation.cloud/workbench?taskId=fdve-329f73f4-e53d-4f75-ace2-c6c16f717e99\u001b\\\u001b[32md-4f75-ace2-c6c16f717e99'\u001b[0m\u001b]8;;\u001b\\.                                         \n"
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     },
     "metadata": {},
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       "Output()"
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     "output_type": "display_data"
    },
    {
     "data": {
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
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     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">13:30:10 EST </span>status = queued                                                    \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m13:30:10 EST\u001b[0m\u001b[2;36m \u001b[0mstatus = queued                                                    \n"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>To cancel the simulation, use <span style=\"color: #008000; text-decoration-color: #008000\">'web.abort(task_id)'</span> or              \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'web.delete(task_id)'</span> or abort/delete the task in the web UI.      \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Terminating the Python script will not stop the job running on the \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>cloud.                                                             \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mTo cancel the simulation, use \u001b[32m'web.abort\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m or              \n",
       "\u001b[2;36m             \u001b[0m\u001b[32m'web.delete\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m or abort/delete the task in the web UI.      \n",
       "\u001b[2;36m             \u001b[0mTerminating the Python script will not stop the job running on the \n",
       "\u001b[2;36m             \u001b[0mcloud.                                                             \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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    {
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      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">13:30:15 EST </span>status = preprocess                                                \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m13:30:15 EST\u001b[0m\u001b[2;36m \u001b[0mstatus = preprocess                                                \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">13:30:18 EST </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.025</span>. Use <span style=\"color: #008000; text-decoration-color: #008000\">'web.real_cost(task_id)'</span> to get\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>the billed FlexCredit cost after a simulation run.                 \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m13:30:18 EST\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m0.025\u001b[0m. Use \u001b[32m'web.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m to get\n",
       "\u001b[2;36m             \u001b[0mthe billed FlexCredit cost after a simulation run.                 \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>starting up solver                                                 \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mstarting up solver                                                 \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>running solver                                                     \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mrunning solver                                                     \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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       "Output()"
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     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">13:30:23 EST </span>early shutoff detected at <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">32</span>%, exiting.                            \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m13:30:23 EST\u001b[0m\u001b[2;36m \u001b[0mearly shutoff detected at \u001b[1;36m32\u001b[0m%, exiting.                            \n"
      ]
     },
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    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
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     },
     "metadata": {},
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    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>status = success                                                   \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mstatus = success                                                   \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>View simulation result at                                          \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-329f73f4-e53d-4f75-ace2-c6c16f717e99\" target=\"_blank\"><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">'https://tidy3d.simulation.cloud/workbench?taskId=fdve-329f73f4-e53</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-329f73f4-e53d-4f75-ace2-c6c16f717e99\" target=\"_blank\"><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">d-4f75-ace2-c6c16f717e99'</span></a><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">.</span>                                         \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mView simulation result at                                          \n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=654369;https://tidy3d.simulation.cloud/workbench?taskId=fdve-329f73f4-e53d-4f75-ace2-c6c16f717e99\u001b\\\u001b[4;34m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=151246;https://tidy3d.simulation.cloud/workbench?taskId=fdve-329f73f4-e53d-4f75-ace2-c6c16f717e99\u001b\\\u001b[4;34mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=654369;https://tidy3d.simulation.cloud/workbench?taskId=fdve-329f73f4-e53d-4f75-ace2-c6c16f717e99\u001b\\\u001b[4;34m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=274211;https://tidy3d.simulation.cloud/workbench?taskId=fdve-329f73f4-e53d-4f75-ace2-c6c16f717e99\u001b\\\u001b[4;34mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=654369;https://tidy3d.simulation.cloud/workbench?taskId=fdve-329f73f4-e53d-4f75-ace2-c6c16f717e99\u001b\\\u001b[4;34m-329f73f4-e53\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=654369;https://tidy3d.simulation.cloud/workbench?taskId=fdve-329f73f4-e53d-4f75-ace2-c6c16f717e99\u001b\\\u001b[4;34md-4f75-ace2-c6c16f717e99'\u001b[0m\u001b]8;;\u001b\\\u001b[4;34m.\u001b[0m                                         \n"
      ]
     },
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    {
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       "version_minor": 0
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       "Output()"
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    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>loading simulation from simulation_data.hdf5                       \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mloading simulation from simulation_data.hdf5                       \n"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">13:30:24 EST </span>Created task <span style=\"color: #008000; text-decoration-color: #008000\">'var eps'</span> with task_id                                \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'fdve-a22f27be-15ef-47df-9295-9517dd74ed5d'</span> and task_type <span style=\"color: #008000; text-decoration-color: #008000\">'FDTD'</span>.  \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m13:30:24 EST\u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'var eps'\u001b[0m with task_id                                \n",
       "\u001b[2;36m             \u001b[0m\u001b[32m'fdve-a22f27be-15ef-47df-9295-9517dd74ed5d'\u001b[0m and task_type \u001b[32m'FDTD'\u001b[0m.  \n"
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     "metadata": {},
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    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>View task using web UI at                                          \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-a22f27be-15ef-47df-9295-9517dd74ed5d\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'https://tidy3d.simulation.cloud/workbench?taskId=fdve-a22f27be-15e</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-a22f27be-15ef-47df-9295-9517dd74ed5d\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">f-47df-9295-9517dd74ed5d'</span></a>.                                         \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mView task using web UI at                                          \n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=952247;https://tidy3d.simulation.cloud/workbench?taskId=fdve-a22f27be-15ef-47df-9295-9517dd74ed5d\u001b\\\u001b[32m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=483630;https://tidy3d.simulation.cloud/workbench?taskId=fdve-a22f27be-15ef-47df-9295-9517dd74ed5d\u001b\\\u001b[32mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=952247;https://tidy3d.simulation.cloud/workbench?taskId=fdve-a22f27be-15ef-47df-9295-9517dd74ed5d\u001b\\\u001b[32m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=261785;https://tidy3d.simulation.cloud/workbench?taskId=fdve-a22f27be-15ef-47df-9295-9517dd74ed5d\u001b\\\u001b[32mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=952247;https://tidy3d.simulation.cloud/workbench?taskId=fdve-a22f27be-15ef-47df-9295-9517dd74ed5d\u001b\\\u001b[32m-a22f27be-15e\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=952247;https://tidy3d.simulation.cloud/workbench?taskId=fdve-a22f27be-15ef-47df-9295-9517dd74ed5d\u001b\\\u001b[32mf-47df-9295-9517dd74ed5d'\u001b[0m\u001b]8;;\u001b\\.                                         \n"
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     },
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       "version_minor": 0
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       "Output()"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
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    {
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">13:30:25 EST </span>status = queued                                                    \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m13:30:25 EST\u001b[0m\u001b[2;36m \u001b[0mstatus = queued                                                    \n"
      ]
     },
     "metadata": {},
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     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>To cancel the simulation, use <span style=\"color: #008000; text-decoration-color: #008000\">'web.abort(task_id)'</span> or              \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'web.delete(task_id)'</span> or abort/delete the task in the web UI.      \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Terminating the Python script will not stop the job running on the \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>cloud.                                                             \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mTo cancel the simulation, use \u001b[32m'web.abort\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m or              \n",
       "\u001b[2;36m             \u001b[0m\u001b[32m'web.delete\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m or abort/delete the task in the web UI.      \n",
       "\u001b[2;36m             \u001b[0mTerminating the Python script will not stop the job running on the \n",
       "\u001b[2;36m             \u001b[0mcloud.                                                             \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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       "Output()"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">13:30:37 EST </span>status = preprocess                                                \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m13:30:37 EST\u001b[0m\u001b[2;36m \u001b[0mstatus = preprocess                                                \n"
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     },
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    {
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
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      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">13:30:38 EST </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.025</span>. Use <span style=\"color: #008000; text-decoration-color: #008000\">'web.real_cost(task_id)'</span> to get\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>the billed FlexCredit cost after a simulation run.                 \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m13:30:38 EST\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m0.025\u001b[0m. Use \u001b[32m'web.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m to get\n",
       "\u001b[2;36m             \u001b[0mthe billed FlexCredit cost after a simulation run.                 \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>starting up solver                                                 \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mstarting up solver                                                 \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>running solver                                                     \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mrunning solver                                                     \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0d3dd6b319284ac8acc310f87db22870",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">13:30:42 EST </span>early shutoff detected at <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">40</span>%, exiting.                            \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m13:30:42 EST\u001b[0m\u001b[2;36m \u001b[0mearly shutoff detected at \u001b[1;36m40\u001b[0m%, exiting.                            \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>status = postprocess                                               \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mstatus = postprocess                                               \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">13:30:43 EST </span>status = success                                                   \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m13:30:43 EST\u001b[0m\u001b[2;36m \u001b[0mstatus = success                                                   \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>View simulation result at                                          \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-a22f27be-15ef-47df-9295-9517dd74ed5d\" target=\"_blank\"><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">'https://tidy3d.simulation.cloud/workbench?taskId=fdve-a22f27be-15e</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-a22f27be-15ef-47df-9295-9517dd74ed5d\" target=\"_blank\"><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">f-47df-9295-9517dd74ed5d'</span></a><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">.</span>                                         \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mView simulation result at                                          \n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=518606;https://tidy3d.simulation.cloud/workbench?taskId=fdve-a22f27be-15ef-47df-9295-9517dd74ed5d\u001b\\\u001b[4;34m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=935109;https://tidy3d.simulation.cloud/workbench?taskId=fdve-a22f27be-15ef-47df-9295-9517dd74ed5d\u001b\\\u001b[4;34mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=518606;https://tidy3d.simulation.cloud/workbench?taskId=fdve-a22f27be-15ef-47df-9295-9517dd74ed5d\u001b\\\u001b[4;34m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=510209;https://tidy3d.simulation.cloud/workbench?taskId=fdve-a22f27be-15ef-47df-9295-9517dd74ed5d\u001b\\\u001b[4;34mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=518606;https://tidy3d.simulation.cloud/workbench?taskId=fdve-a22f27be-15ef-47df-9295-9517dd74ed5d\u001b\\\u001b[4;34m-a22f27be-15e\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=518606;https://tidy3d.simulation.cloud/workbench?taskId=fdve-a22f27be-15ef-47df-9295-9517dd74ed5d\u001b\\\u001b[4;34mf-47df-9295-9517dd74ed5d'\u001b[0m\u001b]8;;\u001b\\\u001b[4;34m.\u001b[0m                                         \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "40a00be5832141b18555a788f749e5b6",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">13:30:44 EST </span>loading simulation from simulation_data.hdf5                       \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m13:30:44 EST\u001b[0m\u001b[2;36m \u001b[0mloading simulation from simulation_data.hdf5                       \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sim_2d_data = web.run(sim_2d, task_name=\"var eps\")\n",
    "sim_1d_data = web.run(sim_1d, task_name=\"var eps\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d6294c1-5023-4c91-a3ef-b0de0b2297fd",
   "metadata": {},
   "source": [
    "### Example 1 Result Comparison\n",
    "\n",
    "Now that both the 2D and 1D simulations have run, we will compare their results to see how representative the 1D simulation is. The results of the 1D approximation retain a reasonable qualitative fidelity to the 2D version."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "0f120c35-9ae2-4a94-8c6c-96c56b1ffc17",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 2, figsize=(12, 5))\n",
    "\n",
    "ax[0].plot(\n",
    "    td.C_0 / sim_1d_data[\"R\"].flux.f,\n",
    "    sim_1d_data[\"R\"].flux.data,\n",
    "    label=\"1D\",\n",
    "    color=\"red\",\n",
    "    linestyle=\"--\",\n",
    ")\n",
    "ax[0].plot(td.C_0 / sim_2d_data[\"R\"].flux.f, sim_2d_data[\"R\"].flux.data, label=\"2D\", color=\"red\")\n",
    "ax[0].set_title(\"2D vs 1D Reflection\")\n",
    "ax[0].set_xlabel(\"Wavelength (µm)\")\n",
    "ax[0].legend()\n",
    "\n",
    "ax[1].plot(\n",
    "    td.C_0 / sim_1d_data[\"R\"].flux.f,\n",
    "    sim_1d_data[\"T\"].flux.data,\n",
    "    label=\"1D\",\n",
    "    color=\"blue\",\n",
    "    linestyle=\"--\",\n",
    ")\n",
    "ax[1].plot(td.C_0 / sim_2d_data[\"R\"].flux.f, sim_2d_data[\"T\"].flux.data, label=\"2D\", color=\"blue\")\n",
    "ax[1].set_title(\"2D vs 1D Transmission\")\n",
    "ax[1].set_xlabel(\"Wavelength (µm)\")\n",
    "ax[1].legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35762762-3dcb-4f44-9ee6-eee60fff9ec2",
   "metadata": {},
   "source": [
    "## Example 2: Ring Resonator"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "826c2941-9f86-4a72-acf8-11b70ef7bbe7",
   "metadata": {},
   "source": [
    "We will now apply this same method to transform a 3D simulation into its 2D approximation. We will use a ring resonator and check the transmission of the through ports of both setups.<br><br>\n",
    "First we quickly build the 3D simulation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "7eb43d86-4e2b-43b4-93ab-ce0ce0be6cc8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Define wavelength/frequency ranges\n",
    "lambda_beg = 1.5\n",
    "lambda_end = 1.6\n",
    "freq_beg = td.C_0 / lambda_end\n",
    "freq_end = td.C_0 / lambda_beg\n",
    "freq0 = (freq_beg + freq_end) / 2\n",
    "fwidth = (freq_end - freq0) / 1.5\n",
    "lambdas_ring = np.linspace(lambda_beg, lambda_end, 301)\n",
    "freqs_ring = td.C_0 / lambdas_ring[::-1]\n",
    "\n",
    "# Refractive indices\n",
    "Si_n = 3.4777\n",
    "SiO2_n = 1.444\n",
    "\n",
    "# Materials used\n",
    "Si = td.Medium(permittivity=Si_n**2)\n",
    "SiO2 = td.Medium(permittivity=SiO2_n**2)\n",
    "\n",
    "# Define geometries and their structures\n",
    "h_clad = 2.8\n",
    "h_box = 2\n",
    "h_wafer = 0.5\n",
    "\n",
    "w_wg = 0.5\n",
    "h_wg = 0.22\n",
    "couple_gap = 0.1\n",
    "ring_radius = 5\n",
    "\n",
    "z_wafer = -h_box - h_wafer / 2\n",
    "\n",
    "y_wg = ring_radius + w_wg / 2 + couple_gap + w_wg / 2\n",
    "\n",
    "waveguide_top = td.Structure(\n",
    "    geometry=td.Box(\n",
    "        center=[0, y_wg, 0],\n",
    "        size=[td.inf, w_wg, h_wg],\n",
    "    ),\n",
    "    medium=Si,\n",
    ")\n",
    "\n",
    "waveguide_bottom = td.Structure(\n",
    "    geometry=td.Box(\n",
    "        center=[0, -y_wg, 0],\n",
    "        size=[td.inf, w_wg, h_wg],\n",
    "    ),\n",
    "    medium=Si,\n",
    ")\n",
    "\n",
    "wg_ring_geo = td.Cylinder(\n",
    "    center=[0, 0, 0], axis=2, radius=ring_radius + w_wg / 2.0, length=h_wg\n",
    ") - td.Cylinder(center=[0, 0, 0], axis=2, radius=ring_radius - w_wg / 2.0, length=w_wg * 1.5)\n",
    "\n",
    "wg_ring = td.Structure(geometry=wg_ring_geo, medium=Si)\n",
    "\n",
    "x_buffer = 2\n",
    "y_buffer = 1\n",
    "z_buffer = 1\n",
    "\n",
    "z_sim = (h_clad - h_box - h_wafer) / 2\n",
    "h_sim = 3\n",
    "l_sim = 2 * ring_radius + 2 * x_buffer\n",
    "w_sim = 2 * ring_radius + 4 * y_buffer\n",
    "\n",
    "# Create Mode Source\n",
    "wg_insert_x = ring_radius + 0.9 * x_buffer\n",
    "mode_plane = td.Box(\n",
    "    center=[-wg_insert_x, y_wg, 0],\n",
    "    size=[0, 2, 2],\n",
    ")\n",
    "mode_source = td.ModeSource(\n",
    "    size=mode_plane.size,\n",
    "    center=mode_plane.center,\n",
    "    source_time=td.GaussianPulse(freq0=freq0, fwidth=fwidth),\n",
    "    mode_index=0,\n",
    "    direction=\"+\",\n",
    "    num_freqs=7,\n",
    ")\n",
    "\n",
    "# Create monitor\n",
    "through_mode_mnt = td.ModeMonitor(\n",
    "    size=mode_plane.size,\n",
    "    center=[wg_insert_x, y_wg, 0],\n",
    "    freqs=freqs_ring,\n",
    "    mode_spec=td.ModeSpec(num_modes=2),\n",
    "    name=\"mode through\",\n",
    ")\n",
    "\n",
    "# Create simulation\n",
    "min_steps_per_wvl = 15\n",
    "run_time = 8e-11\n",
    "\n",
    "sim_ring_3D = td.Simulation(\n",
    "    size=[l_sim, w_sim, h_sim],\n",
    "    center=(0, 0, 0),\n",
    "    structures=[waveguide_top, waveguide_bottom, wg_ring],\n",
    "    grid_spec=td.GridSpec.auto(min_steps_per_wvl=min_steps_per_wvl, wavelength=td.C_0 / freq0),\n",
    "    sources=[mode_source],\n",
    "    monitors=[through_mode_mnt],\n",
    "    run_time=run_time,\n",
    "    medium=SiO2,\n",
    "    symmetry=(0, 0, 1),  # add symmetry to reduce cost\n",
    ")\n",
    "\n",
    "# Visualize the simulation to check\n",
    "sim_ring_3D.plot(z=0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20af0a05-dd59-49a6-8ae3-272ffdd3d61d",
   "metadata": {},
   "source": [
    "Same as before, we'll calculate the variational effective indices at different input wavelengths, this time downsampled to every 10th wavelength."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "77c3ee34-8395-48db-8808-1f5b36cdf091",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
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       "Output()"
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     "output_type": "display_data"
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",
      "text/plain": [
       "<Figure size 300x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "reference_point = (0, -y_wg)\n",
    "waveguide_point = (0, -y_wg)\n",
    "other_point = (0, 0)\n",
    "\n",
    "# Downsample wavelengths to speed up the calculation\n",
    "spectrum = lambdas_ring[::10]\n",
    "ring_waveguide_medium = approximate_material(\n",
    "    sim_ring_3D, waveguide_point, reference_point, spectrum, plot=True\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "4ade732a-6ce6-4847-a5ba-c5923834d667",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "93ca9f9037d9429eb14f7ecea5f776d9",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ring_background_medium = approximate_material(sim_ring_3D, other_point, reference_point, spectrum)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bbda4057-4feb-4350-8de7-ac3d9668ba7f",
   "metadata": {},
   "source": [
    "As before, we create a 2D copy of the simulation and replace the mediums with those that we just calculated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "4a0245f6-7372-4315-a919-1bfd49ea192f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Note: removed symmetry, as it is incompatible with the variational approximation method.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sim_ring_2D = create_2D_sim(sim_ring_3D, [ring_waveguide_medium, ring_background_medium])\n",
    "\n",
    "sim_ring_2D.plot_eps(z=0, freq=freq0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "71d391f2-3935-44a3-9a8d-1cf4abb4e219",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">13:31:11 EST </span>Created task <span style=\"color: #008000; text-decoration-color: #008000\">'2.5D FDTD ring resonator 2D'</span> with task_id            \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'fdve-f748f5dd-5884-4d3e-be98-4eefe1f9df95'</span> and task_type <span style=\"color: #008000; text-decoration-color: #008000\">'FDTD'</span>.  \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m13:31:11 EST\u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'2.5D FDTD ring resonator 2D'\u001b[0m with task_id            \n",
       "\u001b[2;36m             \u001b[0m\u001b[32m'fdve-f748f5dd-5884-4d3e-be98-4eefe1f9df95'\u001b[0m and task_type \u001b[32m'FDTD'\u001b[0m.  \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>View task using web UI at                                          \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-f748f5dd-5884-4d3e-be98-4eefe1f9df95\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'https://tidy3d.simulation.cloud/workbench?taskId=fdve-f748f5dd-588</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-f748f5dd-5884-4d3e-be98-4eefe1f9df95\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">4-4d3e-be98-4eefe1f9df95'</span></a>.                                         \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mView task using web UI at                                          \n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=114736;https://tidy3d.simulation.cloud/workbench?taskId=fdve-f748f5dd-5884-4d3e-be98-4eefe1f9df95\u001b\\\u001b[32m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=24428;https://tidy3d.simulation.cloud/workbench?taskId=fdve-f748f5dd-5884-4d3e-be98-4eefe1f9df95\u001b\\\u001b[32mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=114736;https://tidy3d.simulation.cloud/workbench?taskId=fdve-f748f5dd-5884-4d3e-be98-4eefe1f9df95\u001b\\\u001b[32m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=201938;https://tidy3d.simulation.cloud/workbench?taskId=fdve-f748f5dd-5884-4d3e-be98-4eefe1f9df95\u001b\\\u001b[32mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=114736;https://tidy3d.simulation.cloud/workbench?taskId=fdve-f748f5dd-5884-4d3e-be98-4eefe1f9df95\u001b\\\u001b[32m-f748f5dd-588\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=114736;https://tidy3d.simulation.cloud/workbench?taskId=fdve-f748f5dd-5884-4d3e-be98-4eefe1f9df95\u001b\\\u001b[32m4-4d3e-be98-4eefe1f9df95'\u001b[0m\u001b]8;;\u001b\\.                                         \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7fde33b614374403b37fd1d5478bdaa2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">13:31:13 EST </span>status = queued                                                    \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m13:31:13 EST\u001b[0m\u001b[2;36m \u001b[0mstatus = queued                                                    \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>To cancel the simulation, use <span style=\"color: #008000; text-decoration-color: #008000\">'web.abort(task_id)'</span> or              \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'web.delete(task_id)'</span> or abort/delete the task in the web UI.      \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Terminating the Python script will not stop the job running on the \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>cloud.                                                             \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mTo cancel the simulation, use \u001b[32m'web.abort\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m or              \n",
       "\u001b[2;36m             \u001b[0m\u001b[32m'web.delete\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m or abort/delete the task in the web UI.      \n",
       "\u001b[2;36m             \u001b[0mTerminating the Python script will not stop the job running on the \n",
       "\u001b[2;36m             \u001b[0mcloud.                                                             \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">13:31:26 EST </span>status = preprocess                                                \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m13:31:26 EST\u001b[0m\u001b[2;36m \u001b[0mstatus = preprocess                                                \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">13:31:29 EST </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.203</span>. Use <span style=\"color: #008000; text-decoration-color: #008000\">'web.real_cost(task_id)'</span> to get\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>the billed FlexCredit cost after a simulation run.                 \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m13:31:29 EST\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m0.203\u001b[0m. Use \u001b[32m'web.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m to get\n",
       "\u001b[2;36m             \u001b[0mthe billed FlexCredit cost after a simulation run.                 \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>starting up solver                                                 \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mstarting up solver                                                 \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">13:31:30 EST </span>running solver                                                     \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m13:31:30 EST\u001b[0m\u001b[2;36m \u001b[0mrunning solver                                                     \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "36072bb2e2874ac8b75a30f762b33b7c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">13:32:16 EST </span>early shutoff detected at <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">24</span>%, exiting.                            \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m13:32:16 EST\u001b[0m\u001b[2;36m \u001b[0mearly shutoff detected at \u001b[1;36m24\u001b[0m%, exiting.                            \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>status = postprocess                                               \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mstatus = postprocess                                               \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>status = success                                                   \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mstatus = success                                                   \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>View simulation result at                                          \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-f748f5dd-5884-4d3e-be98-4eefe1f9df95\" target=\"_blank\"><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">'https://tidy3d.simulation.cloud/workbench?taskId=fdve-f748f5dd-588</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-f748f5dd-5884-4d3e-be98-4eefe1f9df95\" target=\"_blank\"><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">4-4d3e-be98-4eefe1f9df95'</span></a><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">.</span>                                         \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mView simulation result at                                          \n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=123452;https://tidy3d.simulation.cloud/workbench?taskId=fdve-f748f5dd-5884-4d3e-be98-4eefe1f9df95\u001b\\\u001b[4;34m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=625074;https://tidy3d.simulation.cloud/workbench?taskId=fdve-f748f5dd-5884-4d3e-be98-4eefe1f9df95\u001b\\\u001b[4;34mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=123452;https://tidy3d.simulation.cloud/workbench?taskId=fdve-f748f5dd-5884-4d3e-be98-4eefe1f9df95\u001b\\\u001b[4;34m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=838700;https://tidy3d.simulation.cloud/workbench?taskId=fdve-f748f5dd-5884-4d3e-be98-4eefe1f9df95\u001b\\\u001b[4;34mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=123452;https://tidy3d.simulation.cloud/workbench?taskId=fdve-f748f5dd-5884-4d3e-be98-4eefe1f9df95\u001b\\\u001b[4;34m-f748f5dd-588\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=123452;https://tidy3d.simulation.cloud/workbench?taskId=fdve-f748f5dd-5884-4d3e-be98-4eefe1f9df95\u001b\\\u001b[4;34m4-4d3e-be98-4eefe1f9df95'\u001b[0m\u001b]8;;\u001b\\\u001b[4;34m.\u001b[0m                                         \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2d72798360574db685e659afa577bd61",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">13:32:17 EST </span>loading simulation from simulation_data.hdf5                       \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m13:32:17 EST\u001b[0m\u001b[2;36m \u001b[0mloading simulation from simulation_data.hdf5                       \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Created task <span style=\"color: #008000; text-decoration-color: #008000\">'2.5D FDTD ring resonator 3D'</span> with task_id            \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'fdve-0f179763-63fa-42ff-9c25-6c48fae8cff1'</span> and task_type <span style=\"color: #008000; text-decoration-color: #008000\">'FDTD'</span>.  \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'2.5D FDTD ring resonator 3D'\u001b[0m with task_id            \n",
       "\u001b[2;36m             \u001b[0m\u001b[32m'fdve-0f179763-63fa-42ff-9c25-6c48fae8cff1'\u001b[0m and task_type \u001b[32m'FDTD'\u001b[0m.  \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>View task using web UI at                                          \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-0f179763-63fa-42ff-9c25-6c48fae8cff1\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'https://tidy3d.simulation.cloud/workbench?taskId=fdve-0f179763-63f</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-0f179763-63fa-42ff-9c25-6c48fae8cff1\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">a-42ff-9c25-6c48fae8cff1'</span></a>.                                         \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mView task using web UI at                                          \n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=166270;https://tidy3d.simulation.cloud/workbench?taskId=fdve-0f179763-63fa-42ff-9c25-6c48fae8cff1\u001b\\\u001b[32m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=636363;https://tidy3d.simulation.cloud/workbench?taskId=fdve-0f179763-63fa-42ff-9c25-6c48fae8cff1\u001b\\\u001b[32mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=166270;https://tidy3d.simulation.cloud/workbench?taskId=fdve-0f179763-63fa-42ff-9c25-6c48fae8cff1\u001b\\\u001b[32m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=964428;https://tidy3d.simulation.cloud/workbench?taskId=fdve-0f179763-63fa-42ff-9c25-6c48fae8cff1\u001b\\\u001b[32mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=166270;https://tidy3d.simulation.cloud/workbench?taskId=fdve-0f179763-63fa-42ff-9c25-6c48fae8cff1\u001b\\\u001b[32m-0f179763-63f\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=166270;https://tidy3d.simulation.cloud/workbench?taskId=fdve-0f179763-63fa-42ff-9c25-6c48fae8cff1\u001b\\\u001b[32ma-42ff-9c25-6c48fae8cff1'\u001b[0m\u001b]8;;\u001b\\.                                         \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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       "model_id": "9f3a2c093e8b4ca9b629d601c528c153",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">13:32:19 EST </span>status = queued                                                    \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m13:32:19 EST\u001b[0m\u001b[2;36m \u001b[0mstatus = queued                                                    \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>To cancel the simulation, use <span style=\"color: #008000; text-decoration-color: #008000\">'web.abort(task_id)'</span> or              \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'web.delete(task_id)'</span> or abort/delete the task in the web UI.      \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Terminating the Python script will not stop the job running on the \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>cloud.                                                             \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mTo cancel the simulation, use \u001b[32m'web.abort\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m or              \n",
       "\u001b[2;36m             \u001b[0m\u001b[32m'web.delete\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m or abort/delete the task in the web UI.      \n",
       "\u001b[2;36m             \u001b[0mTerminating the Python script will not stop the job running on the \n",
       "\u001b[2;36m             \u001b[0mcloud.                                                             \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
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       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">13:32:47 EST </span>status = preprocess                                                \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m13:32:47 EST\u001b[0m\u001b[2;36m \u001b[0mstatus = preprocess                                                \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">13:32:49 EST </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">4.011</span>. Use <span style=\"color: #008000; text-decoration-color: #008000\">'web.real_cost(task_id)'</span> to get\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>the billed FlexCredit cost after a simulation run.                 \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m13:32:49 EST\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m4.011\u001b[0m. Use \u001b[32m'web.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m to get\n",
       "\u001b[2;36m             \u001b[0mthe billed FlexCredit cost after a simulation run.                 \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>starting up solver                                                 \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mstarting up solver                                                 \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>running solver                                                     \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mrunning solver                                                     \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">14:01:38 EST </span>early shutoff detected at <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">56</span>%, exiting.                            \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:01:38 EST\u001b[0m\u001b[2;36m \u001b[0mearly shutoff detected at \u001b[1;36m56\u001b[0m%, exiting.                            \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>status = postprocess                                               \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mstatus = postprocess                                               \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">14:01:40 EST </span>status = success                                                   \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:01:40 EST\u001b[0m\u001b[2;36m \u001b[0mstatus = success                                                   \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
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    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>View simulation result at                                          \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-0f179763-63fa-42ff-9c25-6c48fae8cff1\" target=\"_blank\"><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">'https://tidy3d.simulation.cloud/workbench?taskId=fdve-0f179763-63f</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-0f179763-63fa-42ff-9c25-6c48fae8cff1\" target=\"_blank\"><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">a-42ff-9c25-6c48fae8cff1'</span></a><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">.</span>                                         \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mView simulation result at                                          \n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=682546;https://tidy3d.simulation.cloud/workbench?taskId=fdve-0f179763-63fa-42ff-9c25-6c48fae8cff1\u001b\\\u001b[4;34m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=367402;https://tidy3d.simulation.cloud/workbench?taskId=fdve-0f179763-63fa-42ff-9c25-6c48fae8cff1\u001b\\\u001b[4;34mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=682546;https://tidy3d.simulation.cloud/workbench?taskId=fdve-0f179763-63fa-42ff-9c25-6c48fae8cff1\u001b\\\u001b[4;34m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=643068;https://tidy3d.simulation.cloud/workbench?taskId=fdve-0f179763-63fa-42ff-9c25-6c48fae8cff1\u001b\\\u001b[4;34mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=682546;https://tidy3d.simulation.cloud/workbench?taskId=fdve-0f179763-63fa-42ff-9c25-6c48fae8cff1\u001b\\\u001b[4;34m-0f179763-63f\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=682546;https://tidy3d.simulation.cloud/workbench?taskId=fdve-0f179763-63fa-42ff-9c25-6c48fae8cff1\u001b\\\u001b[4;34ma-42ff-9c25-6c48fae8cff1'\u001b[0m\u001b]8;;\u001b\\\u001b[4;34m.\u001b[0m                                         \n"
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     },
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       "version_minor": 0
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       "Output()"
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     "metadata": {},
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    },
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     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">14:01:41 EST </span>loading simulation from simulation_data.hdf5                       \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:01:41 EST\u001b[0m\u001b[2;36m \u001b[0mloading simulation from simulation_data.hdf5                       \n"
      ]
     },
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   ],
   "source": [
    "sim_data_2D = web.run(sim_ring_2D, task_name=\"2.5D FDTD ring resonator 2D\")\n",
    "sim_data_3D = web.run(sim_ring_3D, task_name=\"2.5D FDTD ring resonator 3D\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3dc98bc3-bbcf-4a4b-8d86-7fab1b9bd0a4",
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   "source": [
    "### Example 2 Result Comparison\n",
    "\n",
    "We will compare the results of our 2D and 3D simulations to see how representative our approximation is. As one can see, the resonant behavior of this setup is well-approximated, especially near the central frequency. The depth of the peaks is lower in the 2D simulation because the 3D simulation takes into account vertical coupling. Toward the ends of the spectrum, there is more error in the free spectral range and resonance frequencies, although this is still well within 1% of the resonant wavelength, as we show for the first resonance. However, the next example will show that this method will not always be a good approximation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "3e23ed0d-1a5f-4a7a-bfd2-ec7a5e3b975e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Difference in first resonant wavelength: 0.0033333333333331883\n"
     ]
    }
   ],
   "source": [
    "sim_data_2D[\"mode through\"].amps.sel(mode_index=0, direction=\"+\").abs.plot(x=\"f\")\n",
    "sim_data_3D[\"mode through\"].amps.sel(mode_index=0, direction=\"+\").abs.plot(x=\"f\")\n",
    "plt.legend([\"2D\", \"3D\"])\n",
    "plt.show()\n",
    "\n",
    "res_lda_2d = (\n",
    "    td.C_0 / sim_data_2D[\"mode through\"].amps.sel(mode_index=0, direction=\"+\").abs.f.data[46]\n",
    ")\n",
    "res_lda_3d = (\n",
    "    td.C_0 / sim_data_3D[\"mode through\"].amps.sel(mode_index=0, direction=\"+\").abs.f.data[56]\n",
    ")\n",
    "res_wavelength_dif = np.abs(res_lda_2d - res_lda_3d)\n",
    "print(f\"Difference in first resonant wavelength: {res_wavelength_dif}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59164d1d-6a30-4c1e-8570-69fb76fccc42",
   "metadata": {},
   "source": [
    "## Strip-to-slot Counter-example\n",
    "\n",
    "This approximation method is not always applicable, and care must be taken to ensure that a given material's approximation is not too far off from the physical constraints of the original material. If not, this can lead to poor approximations. (One can check this by printing out the $n_{eff}^2 + \\frac{num}{denom}$ value calculated in the variational method function above.)\n",
    "<br><br>\n",
    "We will now show an example where this approximation does not work as well. We will use the first design of our [strip to slot converter example](https://www.flexcompute.com/tidy3d/examples/notebooks/StripToSlotConverters/). First we copy the simulation parameters from the notebook, removing the field monitor, as it will be unnecessary for this illustration:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "2b46a885-4127-4b88-b32a-2c8714fe3b93",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Define wavelength/frequency ranges\n",
    "lda0 = 1.55\n",
    "ldas = np.linspace(1.5, 1.6, 101)\n",
    "freq0 = td.C_0 / lda0\n",
    "freqs = td.C_0 / ldas\n",
    "fwidth = 0.5 * (np.max(freqs) - np.min(freqs))\n",
    "\n",
    "# Create materials\n",
    "n_si = 3.47\n",
    "si = td.Medium(permittivity=n_si**2)\n",
    "n_sio2 = 1.44\n",
    "sio2 = td.Medium(permittivity=n_sio2**2)\n",
    "\n",
    "# Define geometry\n",
    "L_mmi = 1.38\n",
    "W_mmi = 1.24\n",
    "L = 6\n",
    "buffer = 5\n",
    "h_si = 0.25\n",
    "w_strip = 0.4\n",
    "w_slot = 0.62\n",
    "g = 0.1\n",
    "\n",
    "vertices = [\n",
    "    (-buffer, w_strip / 2),\n",
    "    (0, w_strip / 2),\n",
    "    (0, W_mmi / 2),\n",
    "    (L_mmi, W_mmi / 2),\n",
    "    (L, w_slot / 2),\n",
    "    (L + buffer, w_slot / 2),\n",
    "    (L + buffer, g / 2),\n",
    "    (L_mmi, g / 2),\n",
    "    (L_mmi, -g / 2),\n",
    "    (L + buffer, -g / 2),\n",
    "    (L + buffer, -w_slot / 2),\n",
    "    (L, -w_slot / 2),\n",
    "    (L_mmi, -W_mmi / 2),\n",
    "    (0, -W_mmi / 2),\n",
    "    (0, -w_strip / 2),\n",
    "    (-buffer, -w_strip / 2),\n",
    "]\n",
    "\n",
    "converter = td.Structure(\n",
    "    geometry=td.PolySlab(vertices=vertices, axis=2, slab_bounds=(-h_si / 2, h_si / 2)), medium=si\n",
    ")\n",
    "\n",
    "# Define mode source\n",
    "mode_spec = td.ModeSpec(num_modes=1, target_neff=n_si)\n",
    "mode_source = td.ModeSource(\n",
    "    center=(-lda0, 0, 0),\n",
    "    size=(0, 4 * w_strip, 6 * h_si),\n",
    "    source_time=td.GaussianPulse(freq0=freq0, fwidth=fwidth),\n",
    "    direction=\"+\",\n",
    "    mode_spec=mode_spec,\n",
    "    mode_index=0,\n",
    "    num_freqs=7,\n",
    ")\n",
    "\n",
    "# Define mode monitor\n",
    "mode_monitor = td.ModeMonitor(\n",
    "    center=(L + lda0, 0, 0),\n",
    "    size=(0, 4 * w_strip, 6 * h_si),\n",
    "    freqs=freqs,\n",
    "    mode_spec=mode_spec,\n",
    "    name=\"mode\",\n",
    ")\n",
    "\n",
    "# Define misc. simulation parameters\n",
    "Lx = L + 3 * lda0\n",
    "Ly = W_mmi + lda0\n",
    "Lz = 10 * h_si\n",
    "sim_size = (Lx, Ly, Lz)\n",
    "\n",
    "run_time = 1e-12\n",
    "\n",
    "# construct simulation\n",
    "sim_splitter_3D = td.Simulation(\n",
    "    center=(L / 2, 0, 0),\n",
    "    size=sim_size,\n",
    "    grid_spec=td.GridSpec.auto(min_steps_per_wvl=30, wavelength=lda0),\n",
    "    structures=[converter],\n",
    "    sources=[mode_source],\n",
    "    monitors=[mode_monitor],\n",
    "    run_time=run_time,\n",
    "    boundary_spec=td.BoundarySpec.all_sides(boundary=td.PML()),\n",
    "    medium=sio2,\n",
    "    symmetry=(0, -1, 1),\n",
    ")\n",
    "\n",
    "# plot the simulation\n",
    "sim_splitter_3D.plot_eps(z=0, freq=freq0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68b9ba6d-af75-47bd-9bf7-9341f3693b97",
   "metadata": {},
   "source": [
    "Now we convert the 3D simulation to a 2D simulation, as before."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "dd794575-4213-4a95-9586-b1e1625eb316",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "887318dc121247b3b2f7d133968f3ac5",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ee23f339c40d455a99bb01c7164a708e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Note: removed symmetry, as it is incompatible with the variational approximation method.\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Approximate materials by downsampling again\n",
    "spectrum = ldas[::15]\n",
    "ref_point = (0, 0)\n",
    "core_point = (0, 0)\n",
    "other_point = (2, 0)\n",
    "\n",
    "waveguide_splitter_mat = approximate_material(sim_splitter_3D, core_point, ref_point, spectrum)\n",
    "background_splitter_mat = approximate_material(sim_splitter_3D, other_point, ref_point, spectrum)\n",
    "\n",
    "sim_splitter_2D = create_2D_sim(sim_splitter_3D, [waveguide_splitter_mat, background_splitter_mat])\n",
    "sim_splitter_2D.plot_eps(z=0, freq=freq0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "f56574ce-a75a-4d93-bab8-8715c24ddbc7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">14:01:49 EST </span>Created task <span style=\"color: #008000; text-decoration-color: #008000\">'2.5D FDTD splitter 2D'</span> with task_id                  \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'fdve-4cb0b3d9-c535-4534-8dc7-04a64653ce14'</span> and task_type <span style=\"color: #008000; text-decoration-color: #008000\">'FDTD'</span>.  \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:01:49 EST\u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'2.5D FDTD splitter 2D'\u001b[0m with task_id                  \n",
       "\u001b[2;36m             \u001b[0m\u001b[32m'fdve-4cb0b3d9-c535-4534-8dc7-04a64653ce14'\u001b[0m and task_type \u001b[32m'FDTD'\u001b[0m.  \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>View task using web UI at                                          \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-4cb0b3d9-c535-4534-8dc7-04a64653ce14\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'https://tidy3d.simulation.cloud/workbench?taskId=fdve-4cb0b3d9-c53</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-4cb0b3d9-c535-4534-8dc7-04a64653ce14\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">5-4534-8dc7-04a64653ce14'</span></a>.                                         \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mView task using web UI at                                          \n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=112189;https://tidy3d.simulation.cloud/workbench?taskId=fdve-4cb0b3d9-c535-4534-8dc7-04a64653ce14\u001b\\\u001b[32m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=746483;https://tidy3d.simulation.cloud/workbench?taskId=fdve-4cb0b3d9-c535-4534-8dc7-04a64653ce14\u001b\\\u001b[32mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=112189;https://tidy3d.simulation.cloud/workbench?taskId=fdve-4cb0b3d9-c535-4534-8dc7-04a64653ce14\u001b\\\u001b[32m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=638851;https://tidy3d.simulation.cloud/workbench?taskId=fdve-4cb0b3d9-c535-4534-8dc7-04a64653ce14\u001b\\\u001b[32mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=112189;https://tidy3d.simulation.cloud/workbench?taskId=fdve-4cb0b3d9-c535-4534-8dc7-04a64653ce14\u001b\\\u001b[32m-4cb0b3d9-c53\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=112189;https://tidy3d.simulation.cloud/workbench?taskId=fdve-4cb0b3d9-c535-4534-8dc7-04a64653ce14\u001b\\\u001b[32m5-4534-8dc7-04a64653ce14'\u001b[0m\u001b]8;;\u001b\\.                                         \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "011f409911a544e683995eb4f19f7db7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">14:01:50 EST </span>status = queued                                                    \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:01:50 EST\u001b[0m\u001b[2;36m \u001b[0mstatus = queued                                                    \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>To cancel the simulation, use <span style=\"color: #008000; text-decoration-color: #008000\">'web.abort(task_id)'</span> or              \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'web.delete(task_id)'</span> or abort/delete the task in the web UI.      \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Terminating the Python script will not stop the job running on the \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>cloud.                                                             \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mTo cancel the simulation, use \u001b[32m'web.abort\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m or              \n",
       "\u001b[2;36m             \u001b[0m\u001b[32m'web.delete\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m or abort/delete the task in the web UI.      \n",
       "\u001b[2;36m             \u001b[0mTerminating the Python script will not stop the job running on the \n",
       "\u001b[2;36m             \u001b[0mcloud.                                                             \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">14:01:55 EST </span>status = preprocess                                                \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:01:55 EST\u001b[0m\u001b[2;36m \u001b[0mstatus = preprocess                                                \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">14:01:56 EST </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.025</span>. Use <span style=\"color: #008000; text-decoration-color: #008000\">'web.real_cost(task_id)'</span> to get\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>the billed FlexCredit cost after a simulation run.                 \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:01:56 EST\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m0.025\u001b[0m. Use \u001b[32m'web.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m to get\n",
       "\u001b[2;36m             \u001b[0mthe billed FlexCredit cost after a simulation run.                 \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>starting up solver                                                 \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mstarting up solver                                                 \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>running solver                                                     \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mrunning solver                                                     \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ec978a9210ce4fb5b47e8979b8c5156e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">14:02:04 EST </span>early shutoff detected at <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">48</span>%, exiting.                            \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:02:04 EST\u001b[0m\u001b[2;36m \u001b[0mearly shutoff detected at \u001b[1;36m48\u001b[0m%, exiting.                            \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>status = postprocess                                               \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mstatus = postprocess                                               \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "model_id": "",
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       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>status = success                                                   \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mstatus = success                                                   \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">14:02:05 EST </span>View simulation result at                                          \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-4cb0b3d9-c535-4534-8dc7-04a64653ce14\" target=\"_blank\"><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">'https://tidy3d.simulation.cloud/workbench?taskId=fdve-4cb0b3d9-c53</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-4cb0b3d9-c535-4534-8dc7-04a64653ce14\" target=\"_blank\"><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">5-4534-8dc7-04a64653ce14'</span></a><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">.</span>                                         \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:02:05 EST\u001b[0m\u001b[2;36m \u001b[0mView simulation result at                                          \n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=132861;https://tidy3d.simulation.cloud/workbench?taskId=fdve-4cb0b3d9-c535-4534-8dc7-04a64653ce14\u001b\\\u001b[4;34m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=668189;https://tidy3d.simulation.cloud/workbench?taskId=fdve-4cb0b3d9-c535-4534-8dc7-04a64653ce14\u001b\\\u001b[4;34mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=132861;https://tidy3d.simulation.cloud/workbench?taskId=fdve-4cb0b3d9-c535-4534-8dc7-04a64653ce14\u001b\\\u001b[4;34m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=984912;https://tidy3d.simulation.cloud/workbench?taskId=fdve-4cb0b3d9-c535-4534-8dc7-04a64653ce14\u001b\\\u001b[4;34mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=132861;https://tidy3d.simulation.cloud/workbench?taskId=fdve-4cb0b3d9-c535-4534-8dc7-04a64653ce14\u001b\\\u001b[4;34m-4cb0b3d9-c53\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=132861;https://tidy3d.simulation.cloud/workbench?taskId=fdve-4cb0b3d9-c535-4534-8dc7-04a64653ce14\u001b\\\u001b[4;34m5-4534-8dc7-04a64653ce14'\u001b[0m\u001b]8;;\u001b\\\u001b[4;34m.\u001b[0m                                         \n"
      ]
     },
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    {
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       "model_id": "c92e1519958146babdda6ab9db74068c",
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       "version_minor": 0
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       "Output()"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>loading simulation from simulation_data.hdf5                       \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mloading simulation from simulation_data.hdf5                       \n"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Created task <span style=\"color: #008000; text-decoration-color: #008000\">'2.5D FDTD splitter 3D'</span> with task_id                  \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'fdve-6d645823-3048-4598-a4c7-8c0959f8685c'</span> and task_type <span style=\"color: #008000; text-decoration-color: #008000\">'FDTD'</span>.  \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'2.5D FDTD splitter 3D'\u001b[0m with task_id                  \n",
       "\u001b[2;36m             \u001b[0m\u001b[32m'fdve-6d645823-3048-4598-a4c7-8c0959f8685c'\u001b[0m and task_type \u001b[32m'FDTD'\u001b[0m.  \n"
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    {
     "data": {
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>View task using web UI at                                          \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-6d645823-3048-4598-a4c7-8c0959f8685c\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'https://tidy3d.simulation.cloud/workbench?taskId=fdve-6d645823-304</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-6d645823-3048-4598-a4c7-8c0959f8685c\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">8-4598-a4c7-8c0959f8685c'</span></a>.                                         \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mView task using web UI at                                          \n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=849190;https://tidy3d.simulation.cloud/workbench?taskId=fdve-6d645823-3048-4598-a4c7-8c0959f8685c\u001b\\\u001b[32m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=771626;https://tidy3d.simulation.cloud/workbench?taskId=fdve-6d645823-3048-4598-a4c7-8c0959f8685c\u001b\\\u001b[32mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=849190;https://tidy3d.simulation.cloud/workbench?taskId=fdve-6d645823-3048-4598-a4c7-8c0959f8685c\u001b\\\u001b[32m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=344104;https://tidy3d.simulation.cloud/workbench?taskId=fdve-6d645823-3048-4598-a4c7-8c0959f8685c\u001b\\\u001b[32mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=849190;https://tidy3d.simulation.cloud/workbench?taskId=fdve-6d645823-3048-4598-a4c7-8c0959f8685c\u001b\\\u001b[32m-6d645823-304\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=849190;https://tidy3d.simulation.cloud/workbench?taskId=fdve-6d645823-3048-4598-a4c7-8c0959f8685c\u001b\\\u001b[32m8-4598-a4c7-8c0959f8685c'\u001b[0m\u001b]8;;\u001b\\.                                         \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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       "version_minor": 0
      },
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       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">14:02:07 EST </span>status = queued                                                    \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:02:07 EST\u001b[0m\u001b[2;36m \u001b[0mstatus = queued                                                    \n"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>To cancel the simulation, use <span style=\"color: #008000; text-decoration-color: #008000\">'web.abort(task_id)'</span> or              \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'web.delete(task_id)'</span> or abort/delete the task in the web UI.      \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Terminating the Python script will not stop the job running on the \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>cloud.                                                             \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mTo cancel the simulation, use \u001b[32m'web.abort\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m or              \n",
       "\u001b[2;36m             \u001b[0m\u001b[32m'web.delete\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m or abort/delete the task in the web UI.      \n",
       "\u001b[2;36m             \u001b[0mTerminating the Python script will not stop the job running on the \n",
       "\u001b[2;36m             \u001b[0mcloud.                                                             \n"
      ]
     },
     "metadata": {},
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      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">14:02:11 EST </span>status = preprocess                                                \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:02:11 EST\u001b[0m\u001b[2;36m \u001b[0mstatus = preprocess                                                \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">14:02:13 EST </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.055</span>. Use <span style=\"color: #008000; text-decoration-color: #008000\">'web.real_cost(task_id)'</span> to get\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>the billed FlexCredit cost after a simulation run.                 \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:02:13 EST\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m0.055\u001b[0m. Use \u001b[32m'web.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m to get\n",
       "\u001b[2;36m             \u001b[0mthe billed FlexCredit cost after a simulation run.                 \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>starting up solver                                                 \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mstarting up solver                                                 \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>running solver                                                     \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mrunning solver                                                     \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">14:02:39 EST </span>early shutoff detected at <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">36</span>%, exiting.                            \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:02:39 EST\u001b[0m\u001b[2;36m \u001b[0mearly shutoff detected at \u001b[1;36m36\u001b[0m%, exiting.                            \n"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>status = postprocess                                               \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mstatus = postprocess                                               \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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       "Output()"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">14:02:43 EST </span>status = success                                                   \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:02:43 EST\u001b[0m\u001b[2;36m \u001b[0mstatus = success                                                   \n"
      ]
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      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
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      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>View simulation result at                                          \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-6d645823-3048-4598-a4c7-8c0959f8685c\" target=\"_blank\"><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">'https://tidy3d.simulation.cloud/workbench?taskId=fdve-6d645823-304</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-6d645823-3048-4598-a4c7-8c0959f8685c\" target=\"_blank\"><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">8-4598-a4c7-8c0959f8685c'</span></a><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">.</span>                                         \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mView simulation result at                                          \n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=963963;https://tidy3d.simulation.cloud/workbench?taskId=fdve-6d645823-3048-4598-a4c7-8c0959f8685c\u001b\\\u001b[4;34m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=427813;https://tidy3d.simulation.cloud/workbench?taskId=fdve-6d645823-3048-4598-a4c7-8c0959f8685c\u001b\\\u001b[4;34mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=963963;https://tidy3d.simulation.cloud/workbench?taskId=fdve-6d645823-3048-4598-a4c7-8c0959f8685c\u001b\\\u001b[4;34m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=904508;https://tidy3d.simulation.cloud/workbench?taskId=fdve-6d645823-3048-4598-a4c7-8c0959f8685c\u001b\\\u001b[4;34mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=963963;https://tidy3d.simulation.cloud/workbench?taskId=fdve-6d645823-3048-4598-a4c7-8c0959f8685c\u001b\\\u001b[4;34m-6d645823-304\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=963963;https://tidy3d.simulation.cloud/workbench?taskId=fdve-6d645823-3048-4598-a4c7-8c0959f8685c\u001b\\\u001b[4;34m8-4598-a4c7-8c0959f8685c'\u001b[0m\u001b]8;;\u001b\\\u001b[4;34m.\u001b[0m                                         \n"
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       "Output()"
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     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "</pre>\n"
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       "\n"
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      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">14:02:44 EST </span>loading simulation from simulation_data.hdf5                       \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:02:44 EST\u001b[0m\u001b[2;36m \u001b[0mloading simulation from simulation_data.hdf5                       \n"
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   ],
   "source": [
    "sim_splitter_data_2D = web.run(sim_splitter_2D, task_name=\"2.5D FDTD splitter 2D\")\n",
    "sim_splitter_data_3D = web.run(sim_splitter_3D, task_name=\"2.5D FDTD splitter 3D\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f23b0d3d-5899-479f-9c8a-a391850dff8d",
   "metadata": {},
   "source": [
    "### Counter-example Results\n",
    "\n",
    "Plotting the mode transmissions from the 2D and 3D simulations, we can see that the 2D transmission is significantly lower than that of the 3D simulation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "323a692c-77d9-4c87-be98-4a69c5501c3d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "T_2D = np.abs(sim_splitter_data_2D[\"mode\"].amps.sel(mode_index=0, direction=\"+\")) ** 2\n",
    "T_3D = np.abs(sim_splitter_data_3D[\"mode\"].amps.sel(mode_index=0, direction=\"+\")) ** 2\n",
    "\n",
    "# plot the coupling efficiency\n",
    "plt.plot(ldas, T_2D, label=\"2D\")\n",
    "plt.plot(ldas, T_3D, label=\"3D\")\n",
    "plt.xlim(1.5, 1.6)\n",
    "plt.ylim(0, 1)\n",
    "plt.title(\"Strip-to-slot Coupling Efficiency\")\n",
    "plt.xlabel(r\"Wavelength ($\\mu m$)\")\n",
    "plt.ylabel(\"Transmission\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa05c4dd-3f8f-4ee5-93b4-6d0bac511c8d",
   "metadata": {},
   "source": [
    "This shows that, depending on the mode profile in z, we can get differences in permittivity from the variational method that do not accurately capture the physics of the original simulation, as mentioned in Hammer & Ivanova. Therefore care must be taken when deciding on this approximation method, and, when possible, a final validation with a 3D simulation should be run."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b19d3188-0686-4aa3-8681-a0b0cce69edb",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "applications": [
   "Passive photonic integrated circuit components"
  ],
  "description": "When doing design exploration, one can greatly reduce the amount of time and computational power needed by running 2D approximations of 3D simulations. This notebook shows how to use the variational method to calculate the effective permittivity of materials used in certain 3D simulations, and makes 2D approximations of those simulations.",
  "feature_image": "./img/effective_index_approximation.png",
  "features": [
   "GDS component",
   "Material fitting",
   "2D simulation"
  ],
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "keywords": "variational, effective, index, approximation, 2D, two-dimensional, vertical, mode, permittivity",
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.8"
  },
  "title": "How to approximate 3D simulations with effective permittivity in Tidy3D FDTD"
 },
 "nbformat": 4,
 "nbformat_minor": 5
}