EdgeCoupler.ipynb

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   "source": [
    "# Inverse taper edge coupler "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note: The cost of running the entire notebook is higher than 1 FlexCredit.\n",
    "\n",
    "Fiber-to-chip couplers are essential components in photonic integrated circuits (PICs) that need to couple in/out light using optical fibers. However, the huge mismatch between the mode sizes of a single mode fiber ($\\approx 10\\mu m$ in diameter) and a silicon wire waveguide (220 x 500 nm) can cause significant coupling loss if they are coupled directly. For this reason, the design of the fiber-to-chip coupling devices is critical for the whole system's performance and needs to be carefully accomplished. There are two main categories of fiber-to-chip optical coupling devices: [grating couplers](https://www.flexcompute.com/tidy3d/examples/notebooks/GratingCoupler/), where the optical fiber is positioned perpendicularly to the chip plane; and edge couplers, which allow in-plane light coupling by the direct alignment between the fiber and the integrated waveguide facets. \n",
    "\n",
    "In this notebook, we will show an example of using Tidy3D to evaluate the performance of edge couplers built using inverted taper mode transformers of linear, quadratic, and exponential profiles. We will also see how to set up a [Gaussian beam](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.GaussianBeam.html) to simulate the field launched by a lensed fiber and the use of [Batch](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.web.api.container.Batch.html) simulations to perform parameter sweeps.\n",
    "\n",
    "For more integrated photonic examples such as the [8-Channel mode and polarization de-multiplexer](https://www.flexcompute.com/tidy3d/examples/notebooks/8ChannelDemultiplexer/), the [broadband bi-level taper polarization rotator-splitter](https://www.flexcompute.com/tidy3d/examples/notebooks/BilevelPSR/), and the [broadband directional coupler](https://www.flexcompute.com/tidy3d/examples/notebooks/BroadbandDirectionalCoupler/), please visit our [examples page](https://www.flexcompute.com/tidy3d/examples/). If you are new to the finite-difference time-domain (FDTD) method, we highly recommend going through our [FDTD101](https://www.flexcompute.com/fdtd101/) tutorials. FDTD simulations can diverge due to various reasons. If you run into any simulation divergence issues, please follow the steps outlined in our [troubleshooting guide](https://www.flexcompute.com/tidy3d/examples/notebooks/DivergedFDTDSimulation/) to resolve it."
   ]
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   "source": [
    "We start by importing the necessary modules:"
   ]
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   "source": [
    "# standard python imports\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# Tidy3D imports\n",
    "import tidy3d as td\n",
    "import tidy3d.web as web"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Simulation Setup\n",
    "\n",
    "The inverted taper structure is made using an SOI platform consisting of a silicon (Si) substrate, a 3 $\\mu m$ thick silicon dioxide (SiO2) layer, and a 220 nm thick silicon layer covered by 3 $\\mu m$ thick silicon dioxide. As shown in the image below, the inverted taper is defined at the 220 nm thick Si layer, and its main geometric parameters are also defined in the figure. An optical fiber mode with a Gaussian profile (2.5 $\\mu m$ spot-size) is focused at the taper tip and coupled into the output waveguide.   \n",
    "\n",
    "<img src=\"img/EdgeCoupler.png\" alt=\"Schematic of the inverted taper edge coupler\"  width=\"400\"/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Definition of the simulation wavelength and materials."
   ]
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    "wl_c = 1.55  # Central wavelength.\n",
    "wl_bw = 0.100  # Wavelength bandwidth.\n",
    "wl_n = 101  # Number of wavelength points to compute the output data.\n",
    "\n",
    "mat_si = td.Medium(permittivity=3.48**2)  # Taper and substrate material.\n",
    "mat_sio2 = td.Medium(permittivity=1.44**2)  # Box and cladding material.\n",
    "mat_air = td.Medium(permittivity=1.00)  # External medium material."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Definition of the taper size, lensed fiber parameters, and SOI structure."
   ]
  },
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    "taper_l = 25  # Taper length.\n",
    "taper_w_in = 0.19  # Taper tip width.\n",
    "taper_w_out = 0.50  # Taper output width.\n",
    "taper_t = 0.22  # Taper thickness.\n",
    "\n",
    "# Spot size of the gaussian mode launched by the lensed fiber at the taper tip.\n",
    "spot_size = 2.5\n",
    "\n",
    "box_thick = 3.0  # Silicon dioxide box layer.\n",
    "clad_thick = 3.0  # Silicon dioxide layer covering the taper."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculated parameters and constants used in the simulation set up."
   ]
  },
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   "source": [
    "# Extra space around the taper at x,y directions.\n",
    "pad_x = 1 * wl_c\n",
    "pad_y = 1.5 * wl_c\n",
    "\n",
    "# Wavelength and frequency values.\n",
    "wl_range = np.linspace(wl_c - wl_bw / 2, wl_c + wl_bw / 2, wl_n)\n",
    "freq_c = td.C_0 / wl_c\n",
    "freq_range = td.C_0 / wl_range\n",
    "freq_width = 0.5 * (np.max(freq_range) - np.min(freq_range))\n",
    "run_time = 30 / freq_width\n",
    "\n",
    "# Large number to be used in replacement of td.inf when necessary.\n",
    "_inf = 1e3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Function to create the inverted taper geometry based on the work of [G. Ren et al. (2013)](https://www.sciencedirect.com/science/article/abs/pii/S0030401811006389)."
   ]
  },
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   "source": [
    "# List of inverted tapers names.\n",
    "tap_names = [\"linear\", \"quadratic\", \"exponential\"]\n",
    "\n",
    "\n",
    "def get_taper(\n",
    "    taper_shape=\"linear\",\n",
    "    init_coord=[pad_x, taper_w_in / 2],\n",
    "    end_coord=[pad_x + taper_l, taper_w_out / 2],\n",
    "):\n",
    "    \"\"\"Return a polygon composed of the taper vertices given the desired shape.\"\"\"\n",
    "\n",
    "    x0 = init_coord[0]\n",
    "    x1 = end_coord[0]\n",
    "    y0 = init_coord[1]\n",
    "    y1 = end_coord[1]\n",
    "    x = np.linspace(x0, x1, 41)\n",
    "\n",
    "    tap_name = \"linear_tap\"\n",
    "    if taper_shape == \"quadratic\":\n",
    "        alpha = ((x - x0) / (x1 - x0)) ** 2\n",
    "        tap_name = \"quadratic_tap\"\n",
    "    elif taper_shape == \"exponential\":\n",
    "        alpha = np.expm1((x - x0) / (x1 - x0)) / np.expm1(1)\n",
    "        tap_name = \"exponential_tap\"\n",
    "    elif taper_shape == \"linear\":\n",
    "        alpha = (x - x0) / (x1 - x0)\n",
    "    else:\n",
    "        print(\n",
    "            \"taper_shape is neither 'linear', 'quadratic', or 'exponential'!\\n\"\n",
    "            + \"Linear taper shape returned!\"\n",
    "        )\n",
    "        alpha = (x - x0) / (x1 - x0)\n",
    "\n",
    "    y = y0 + alpha * (y1 - y0)\n",
    "    upper_bound = [[xv, yv] for xv, yv in zip(x, y)] + [[_inf, y1]]\n",
    "    lower_bound = [[_inf, -y1]] + [[xv, -yv] for xv, yv in zip(x[::-1], y[::-1])]\n",
    "    taper_polygon = upper_bound + lower_bound\n",
    "\n",
    "    # Inverted taper structure using a PolySlab.\n",
    "    taper = td.Structure(\n",
    "        geometry=td.PolySlab(\n",
    "            vertices=taper_polygon, axis=2, slab_bounds=(-taper_t / 2, taper_t / 2)\n",
    "        ),\n",
    "        medium=mat_si,\n",
    "        name=tap_name,\n",
    "    )\n",
    "    return taper"
   ]
  },
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   "cell_type": "markdown",
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   "source": [
    "Function to create the simulation object. Since the box and cladding oxide layers are considerably thick, the silicon substrate and the upper air layer will be removed from the simulation, so being possible to take advantage of the symmetry along the z-axis. In addition, we will insert the Gaussian source directly into the taper tip to reduce the simulation cost further."
   ]
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    "def get_simulations(\n",
    "    tap_length=taper_l,\n",
    "    tap_w_in=taper_w_in,\n",
    "    tap_w_out=taper_w_out,\n",
    "    tap_t=taper_t,\n",
    "    tap_names=tap_names,\n",
    "):\n",
    "    \"\"\"Return a dict of simulation objects for the content of 'tap_names'.\"\"\"\n",
    "\n",
    "    size_x = tap_length + 2 * pad_x\n",
    "    size_y = tap_w_out + 2 * pad_y\n",
    "    size_z = box_thick + clad_thick + tap_t\n",
    "\n",
    "    # Gaussian source focused at the taper tip.\n",
    "    # The Gaussian source must be placed at a plane within a uniform medium.\n",
    "    gauss_source = td.GaussianBeam(\n",
    "        center=(0.99 * pad_x, 0, 0),\n",
    "        size=(0, size_y, size_z),\n",
    "        source_time=td.GaussianPulse(freq0=freq_c, fwidth=freq_width),\n",
    "        pol_angle=0,\n",
    "        direction=\"+\",\n",
    "        num_freqs=7,\n",
    "        waist_radius=spot_size / 2,\n",
    "    )\n",
    "\n",
    "    # Field monitor to visualize the fields throughout the length of the taper.\n",
    "    field_monitor_xy = td.FieldMonitor(\n",
    "        center=(size_x / 2, 0, 0),\n",
    "        size=(size_x, size_y, 0),\n",
    "        freqs=[freq_c],\n",
    "        name=\"field_xy\",\n",
    "    )\n",
    "\n",
    "    # Field monitor to visualize the Gaussian input fields.\n",
    "    field_input = td.FieldMonitor(\n",
    "        center=(pad_x, 0, 0),\n",
    "        size=(0, size_y, size_z),\n",
    "        freqs=[freq_c],\n",
    "        name=\"field_input\",\n",
    "    )\n",
    "\n",
    "    # Field monitor to visualize the fields at the output waveguide.\n",
    "    field_output = td.FieldMonitor(\n",
    "        center=(size_x - pad_x / 2, 0, 0),\n",
    "        size=(0, size_y, size_z),\n",
    "        freqs=[freq_c],\n",
    "        name=\"field_output\",\n",
    "    )\n",
    "\n",
    "    # Mode monitor to get the power coupled into the fundamental TE mode.\n",
    "    mode_spec = td.ModeSpec(num_modes=1)\n",
    "    mode_monitor = td.ModeMonitor(\n",
    "        center=(size_x - pad_x / 2, 0, 0),\n",
    "        size=(0, size_y, size_z),\n",
    "        freqs=freq_range,\n",
    "        mode_spec=mode_spec,\n",
    "        name=\"mode_0\",\n",
    "    )\n",
    "\n",
    "    # Silicon dioxide box + cladding layers\n",
    "    sio2_medium = td.Structure(\n",
    "        geometry=td.Box.from_bounds(rmin=(pad_x, -_inf, -_inf), rmax=(_inf, _inf, _inf)),\n",
    "        medium=mat_sio2,\n",
    "    )\n",
    "\n",
    "    # Simulation definition\n",
    "    sim_tap = []\n",
    "    for t_name in tap_names:\n",
    "        taper_poly = get_taper(\n",
    "            t_name,\n",
    "            init_coord=[pad_x, tap_w_in / 2],\n",
    "            end_coord=[size_x - pad_x, tap_w_out / 2],\n",
    "        )\n",
    "\n",
    "        sim_tap.append(\n",
    "            td.Simulation(\n",
    "                center=(size_x / 2, 0, 0),\n",
    "                size=(size_x, size_y, size_z),\n",
    "                medium=mat_air,\n",
    "                grid_spec=td.GridSpec.auto(min_steps_per_wvl=15, wavelength=wl_c),\n",
    "                structures=[sio2_medium, taper_poly],\n",
    "                sources=[gauss_source],\n",
    "                normalize_index=0,\n",
    "                monitors=[field_monitor_xy, field_input, field_output, mode_monitor],\n",
    "                boundary_spec=td.BoundarySpec.all_sides(boundary=td.PML(num_layers=20)),\n",
    "                symmetry=(0, -1, 1),\n",
    "                run_time=run_time,\n",
    "            )\n",
    "        )\n",
    "    sims = dict(zip(tap_names, sim_tap))\n",
    "    return sims"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comparison between inverted tapers with different shapes.\n",
    "\n",
    "We start our analysis by comparing the response of inverted tapers with 3 different geometries: linear, quadratic, and exponential. Then, before running the simulations, we will visualize the shapes of the inverted tapers and examine the simulation set up to see if everything is correct."
   ]
  },
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       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">fields on the Yee grid instead.                                    </span>\n",
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       "\u001b[2;36m13:33:51 PDT\u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING: Default value for the field monitor \u001b[0m\u001b[32m'colocate'\u001b[0m\u001b[31m setting has\u001b[0m\n",
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       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">changed to </span><span style=\"color: #008000; text-decoration-color: #008000\">'True'</span><span style=\"color: #800000; text-decoration-color: #800000\"> in Tidy3D </span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2.4</span><span style=\"color: #800000; text-decoration-color: #800000\">.</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span><span style=\"color: #800000; text-decoration-color: #800000\">. All field components will be    </span>\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><span style=\"color: #800000; text-decoration-color: #800000\">WARNING: Default value for the field monitor </span><span style=\"color: #008000; text-decoration-color: #008000\">'colocate'</span><span style=\"color: #800000; text-decoration-color: #800000\"> setting has</span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">changed to </span><span style=\"color: #008000; text-decoration-color: #008000\">'True'</span><span style=\"color: #800000; text-decoration-color: #800000\"> in Tidy3D </span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2.4</span><span style=\"color: #800000; text-decoration-color: #800000\">.</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span><span style=\"color: #800000; text-decoration-color: #800000\">. All field components will be    </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">colocated to the grid boundaries. Set to </span><span style=\"color: #008000; text-decoration-color: #008000\">'False'</span><span style=\"color: #800000; text-decoration-color: #800000\"> to get the raw    </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">fields on the Yee grid instead.                                    </span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING: Default value for the field monitor \u001b[0m\u001b[32m'colocate'\u001b[0m\u001b[31m setting has\u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31mchanged to \u001b[0m\u001b[32m'True'\u001b[0m\u001b[31m in Tidy3D \u001b[0m\u001b[1;36m2.4\u001b[0m\u001b[31m.\u001b[0m\u001b[1;36m0\u001b[0m\u001b[31m. All field components will be    \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31mcolocated to the grid boundaries. Set to \u001b[0m\u001b[32m'False'\u001b[0m\u001b[31m to get the raw    \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31mfields on the Yee grid instead.                                    \u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get the list of simulation objects.\n",
    "sim_tap = get_simulations(tap_length=taper_l)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-18T17:42:45.515549Z",
     "iopub.status.busy": "2023-08-18T17:42:45.515349Z",
     "iopub.status.idle": "2023-08-18T17:42:45.925574Z",
     "shell.execute_reply": "2023-08-18T17:42:45.925010Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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kqXMMOJIkqXMMOJK2rCTPaL5A8IjmCdvXJXlq2+2S1D4f9CdpS0vyG8ARwDcAB6rqt1pukqRNwIAjaUtrvntuH/Ag8ANVNddykyRtAk5RSdrqjgWOBI5iMJIjSY7gSNrakuwFrgBOAU6sqvNbbpKkTWC67QZI0uFK8lLgkap6R5Ie8A9JfrCqPth22yS1yxEcSZLUOa7BkSRJnWPAkSRJnWPAkSRJnWPAkSRJnWPAkSRJnWPAkSRJnWPAkSRJnWPAkSRJnWPAkSRJnWPAkSRJnWPAkSRJnWPAkSRJnWPAkbTlJbk5yRnr+H7/Nsn16/V+kibPgCNp20tSSb51fr+q/q6qvr3NNklaGwOOpE5LMt12GyRNngFH0oZJ8vQkH0/yQJIrk1yR5DeSnJvk70fqLoyiJPmRJP+U5MtJbk1y0Ujdf5/kliT3Jrlw5NhFSa5O8rYkXwbOTXJakn9Mcn+S25P8lyQ7mvofbk79ZJKvJHlBktOTHBh6z5OT/EWSu5tr/pcN6C5J68iAI2lDNAHiL4G3AscA7wR+apWnfxV4KfBNwI8Ar0jy4837PgV4A/DvgccDxwJPGDn/LODq5vy3A3PA/wYcB3w/8BzgFwCq6lnNOd9VVUdW1ZUjv0cP+CvgFmA3cBJwxSp/D0ktMeBI2ijfB8wAf1hVj1TV1cC+1ZxYVX9bVZ+uqn5VfQr4M+DZzeGfBv6qqj5cVQ8B/yfQH3mLf6yqv2zO/3pVXVtVH62q2aq6GfjjofdbyWkMgtR/rKqvVtWDVfX3K50kqV3OTUvaKI8HbquqGiq7ZTUnJvle4GLgqcAOYCeDEaD59711vm5VfTXJvSNvcevwTpJvA34f2AN8I4N/+65d5e9xMnBLVc2usr6kTcARHEkb5XbgpCQZKtvVvH6VQdAAIMm3jJz7DmAvcHJVPQ54IzD/PrczCB3z534jg2mqYTWy/wbgvwOnVtVjgV8ber+V3ArscrGytLUYcCRtlH8EZoH/kGQmyU8ymO4B+CTwHUmeluQI4KKRc48C7quqB5OcBrx46NjVwPOTPLNZ5/NaVv637Cjgy8BXkvwb4BUjx+8EnrTEuf+NQai6OMljkhyR5H9c4XqSWmbAkbQhquph4CeBc4H7gBcAf9Ecu4FBMPkb4EZgdE3LLwCvTfIA8GrgqqH3vQ74RQajPLcDXwQOsLxfZRCSHgDeBFw5cvwi4PLmU1Znj/wec8CPAt8KfKG51gtWuJ6klmXx9LgkbZwkfwocqKpfb7stkrrNERxJktQ5BhxJktQ5TlFJkqTOcQRHkiR1Tuee63DcccfV7t27J3rNL9zzwESvJ0mSBu6+5YZ7qur40fLOBZzdu3ezf//+iV7zFZd9YKLXkyRJA2982Rljn5DuFJUkSeocA44kSeocA44kSeocA44kSeocA44kSeocA44kSeocA44kSeocA44kSeocA44kSeocA44kSeocA44kSeqczn0XlSRJ2mBV7Jjrc8TsLEfMznHE7Cw75+bYOTtH2m5bw4AjSdI21usPgsrO2TmOmJ1j59zc0P7sorKFMDM7t+mngAw4kiRtdVVM94udc4uDys5FAWWwf3B7EFamq9pu/YZoNeAkORP4I6AH/ElVXTxy/FeAlwOzwN3Az1bV2K9FlyRpq0sVOxfCycGQMr9/xJiy+deuBpXD1VrASdIDXg88FzgA7Euyt6o+O1Ttn4A9VfW1JK8ALgFeMPnWSpK0OlP9YsfcXDOa0l8IJDvm+kuGk/nXHf1+283vjDZHcE4DbqqqzwMkuQI4C1gIOFX1oaH6HwVeMtEWSpK2n2YB7c65uUFQmW1eR7Z3zPVHgssg1Mz0HUnZDNoMOCcBtw7tHwC+d5n6LwP+etyBJOcB5wHs2rVrvdonSdqCev0+O5oAMhxQFvbn5vfnj42W9zfNJ4F0+LbEIuMkLwH2AM8ed7yqLgUuBdizZ4/RWZK2omah7I65xYHk0WzvnJuj51oU0W7AuQ04eWj/CU3ZIknOAC4Enl1VD02obZKk1apiut9nx1yfmf7B0DEz118UQGZGRlZm5kb2+3P0zCZaJ20GnH3AqUlOYRBsXgi8eLhCkqcDfwycWVV3Tb6JktRRVU3g6DPThI+ZhaBxMIAstT1ab7M/E0XbT2sBp6pmk5wPvJfBx8Qvq6rrkrwW2F9Ve4HfAY4E3pkE4AtV9WNttVmSWlHFVBNI5oPGcCg5+DocRPpMN6MpM0MjKwdHWvy0jrqt1TU4VXUNcM1I2auHts+YeKMk6XBV0atipgkS88Fjun/o/uLtuUVBZfj4TH8QVJy6kR6dLbHIWJLWRRNApkeCx3S/FkLFQhgZCSbDdYdDyGhdp2qkzcGAI6ldzSdnejUID72hIDEoOxhApvt9ekPb8+Xz504vCiKHlvl8Emn7MOBIWhjZ6DWhorcocCxfNggYtRA+RoPK8LHh/YU6fqRX0gYw4EiTVMVUwVQVUzX4Yz/VBIupoZAx2O4vKh/U64/UWVzWm3/f/qGBZWpoemZxaCmfGyKpcww42ryqCIMvn5sqCIM/0lkICNWUDe3XyH5zznDZUnXH/fRWqDMfHMafNya8GCQkaSIMOGv09X+9h2+754tDJYv/gC31uO8sVKtD6mToLdK833xZ5s9ZZj/NH9FDtw9eLwvh4eA1purge2Xhdbju0q9TzfXnQ8V8CDm03mB7PqgMh5ZDAswSfSdJ0koMOGv0wGf+hR+8+daVK0qSpInxP5IlSVLnGHAkSVLnGHAkSVLnGHAkSVLnGHAkSVLnGHAkSVLntBpwkpyZ5PokNyW5YMzxnUmubI5/LMnuFpopSZK2mNYCTpIe8HrgecBTgBclecpItZcBX6yqbwX+APjtybZSkiRtRSsGnCS/lOToDbj2acBNVfX5qnoYuAI4a6TOWcDlzfbVwHOSLPVwYEmSJGB1IzgnAPuSXNVMKa1XwDgJGH4E8IGmbGydqpoFvgQcO/pGSc5Lsj/J/rvvvnudmidJkraqFQNOVf06cCrwZuBc4MYkv5nkyRvctlWrqkurak9V7Tn++OPbbo4kSWrZqtbgVFUBdzQ/s8DRwNVJLlnDtW8DTh7af0JTNrZOkmngccC9a7imJEnaBlazBueVSa4FLgE+AnxnVb0C+B7gp9Zw7X3AqUlOSbIDeCGwd6TOXuCcZvungQ82YUuSJGlJq/k28WOAn6yqW4YLq6qf5PmHe+Gqmk1yPvBeoAdcVlXXJXktsL+q9jKYFntrkpuA+xiEIEmSpGWtGHCq6jXLHPvcWi5eVdcA14yUvXpo+0HgZ9ZyDUmStP34JGNJktQ5BhxJktQ5BhxJktQ5BhxJktQ5q/kUlZbxDbtO4FPffNzCflj5U+wZqjJ4LHQtcawWlWWhTq24P7o9eI/h7cH7p4Zfh99rcdm416mm7lSN1h3sT1WZoCVJrTDgrNFR/2YX/7Dr8W03Y3OrYmooSE0NhaBQCwFpvnyqBkFpaihILXV8uM7Cfi3eH1dnqmnToWXjfmCq+ofU7/UX1+sNBUtJUrsMONp4Cf0A2+DPf5YIQL2R8l71F9XpjZzTq6LX7x9ybLDdH9oe1OsNHZ/fn6piun/wOtM+I1PSNmLAkdZRJcwlzAGP9NpuzYgaDk+DkDUainr9g6FoelFw6i8cGy6b7h88d7q/uM78/qDeoG73I66kzcKAI20XCf2EPi2Fr4VRpYPhaBCChrbr0LLh8un++PNGj033+45YSducAUfSZDQB6+EpGHw7ywZrwtTiALQ4BM2MKZ+ZW1x/uM7gWC2U9wxR0qZlwJHUTQmzvTDb27jP8qVqIRDNLApBB8uGjy9Vd6Y/x8zcwToz/b7TedIaGXAk6TBVwsPTPR5e7xGpodGnQeiZWxSQdswdDEUH9/tM9+cG23OHnrej31/fNkqbnAFHkjabodGnB9frPasWjRDNzM0tBKP5IDQIR3OLAtS47R1zTs9p82sl4CQ5BrgS2A3cDJxdVV8cqfM04A3AY4E54HVVdeVEGypJXZHwSK/HI731GW2a6i8OPDvm5kPQfFg6tHy0/nzAkjZCWyM4FwAfqKqLk1zQ7L9qpM7XgJdW1Y1JHg9cm+S9VXX/hNsqSRrRn5riwakpHlzjn5H5dUyDwDMcfh7dtk9N16i2As5ZwOnN9uXA3zIScKrqhqHtf01yF3A8cP9EWihJ2nAL65im1zCy1DxGYOei4HMw/OycPbi/c+j4oLw5x5Gkzmkr4JxQVbc323cAJyxXOclpwA7gn5c4fh5wHsCuXbvWsZmSpE0vYbbXY7bX46vMHN5bVB0SfHYO7e9cdGx+v79wzKm2zWfDAk6SvwG+ZcyhC4d3qqqSLLlaLcmJwFuBc6pq7B1UVZcClwLs2bPHlW+SpEelEh6anuah6Wke2Pnoz59fk7RzKBDtnJ0dKptdFIqOWNifY+ec4WgjbFjAqaozljqW5M4kJ1bV7U2AuWuJeo8F3gNcWFUf3aCmSpK0JgtrkmYe/Z/V+dGjg8Fo9HUQho6YHT02y0zf/6ZfSltTVHuBc4CLm9d3j1ZIsgN4F/CWqrp6ss2TJGkyhkePHq2pfn8k/MwuCkKD7aZs7uD2dlhz1FbAuRi4KsnLgFuAswGS7AF+vqpe3pQ9Czg2ybnNeedW1Scm31xJkjaf/tQUX5+a4uszj27t0VR/MGp0xEL4ORiMDt0/WGcrjRi1EnCq6l7gOWPK9wMvb7bfBrxtwk2TJKnz+lPhwanpRz2l1uv3DwaeofBzxNAoEWyOEOSTjCVJ0qrMTU3x1R07+OqOtluyMp+NJEmSOseAI0mSOseAI0mSOseAI0mSOseAI0mSOseAI0mSOseAI0mSOseAI0mSOseAI0mSOseAI0mSOseAI0mSOseAI0mSOidVm+NbP9dLkgeA69tuxzZxHHBP243YJuzrybCfJ8e+nozt0M9PrKrjRwu7+G3i11fVnrYbsR0k2W9fT4Z9PRn28+TY15OxnfvZKSpJktQ5BhxJktQ5XQw4l7bdgG3Evp4c+3oy7OfJsa8nY9v2c+cWGUuSJHVxBEeSJG1zBhxJktQ5nQo4Sc5Mcn2Sm5Jc0HZ7uizJzUk+neQTSfa33Z6uSHJZkruSfGao7Jgk709yY/N6dJtt7Iol+vqiJLc19/Unkvxwm23sgiQnJ/lQks8muS7JK5ty7+t1tkxfb8v7ujNrcJL0gBuA5wIHgH3Ai6rqs602rKOS3AzsqaquP0BqopI8C/gK8JaqempTdglwX1Vd3AT3o6vqVW22swuW6OuLgK9U1e+22bYuSXIicGJVfTzJUcC1wI8D5+J9va6W6euz2Yb3dZdGcE4Dbqqqz1fVw8AVwFktt0l6VKrqw8B9I8VnAZc325cz+AdLa7REX2udVdXtVfXxZvsB4HPASXhfr7tl+npb6lLAOQm4dWj/ANv4f9gJKOB9Sa5Ncl7bjem4E6rq9mb7DuCENhuzDZyf5FPNFJbTJusoyW7g6cDH8L7eUCN9Ddvwvu5SwNFkPbOqvht4HvCLzXC/NlgN5pS7Ma+8Ob0BeDLwNOB24PdabU2HJDkS+HPgl6vqy8PHvK/X15i+3pb3dZcCzm3AyUP7T2jKtAGq6rbm9S7gXQymCLUx7mzm1ufn2O9quT2dVVV3VtVcVfWBN+F9vS6SzDD4g/v2qvqLptj7egOM6+vtel93KeDsA05NckqSHcALgb0tt6mTkjymWcBGkscAPwR8ZvmztAZ7gXOa7XOAd7fYlk6b/4Pb+Am8r9csSYA3A5+rqt8fOuR9vc6W6uvtel935lNUAM1H3/4Q6AGXVdXr2m1RNyV5EoNRGxh8I/077Ov1keTPgNOB44A7gdcAfwlcBewCbgHOrioXx67REn19OoNh/AJuBn5uaJ2IDkOSZwJ/B3wa6DfFv8ZgbYj39Tpapq9fxDa8rzsVcCRJkqBbU1SSJEmAAUeSJHWQAUeSJHWOAUeSJHWOAUeSJHWOAUeSJHWOAUeSJHWOAUfSlpXkGc0XCB7RPGH7uiRPbbtdktrng/4kbWlJfgM4AvgG4EBV/VbLTZK0CRhwJG1pzXfP7QMeBH6gquZabpKkTcApKklb3bHAkcBRDEZyJMkRHElbW5K9wBXAKcCJVXV+y02StAlMt90ASTpcSV4KPFJV70jSA/4hyQ9W1QfbbpukdjmCI0mSOsc1OJIkqXMMOJIkqXMMOJIkqXMMOJIkqXMMOJIkqXMMOJIkqXMMOJIkqXMMOJIkqXMMOJIkqXMMOJIkqXMMOJIkqXMMOJIkqXMMOJK0hCT/Lsn7Vln3oiRv2+g2SVodA44kAUl2J6kk0/NlVfX2qvqhNtsl6fAYcCRJUucYcCStSZLHJ/nzJHcn+Zck/yHJMUkOJPnRps6RSW5K8tJm/0+TvDHJ+5M8kOT/S/LEoff8gST7knypef2BoWN/m+Q/J/lIc+77khw3dPz7kvxDkvuTfDLJ6as898PN6/1JvpLk+5Ocm+Tvh87/oyS3JvlykmuT/NuN6FNJa2fAkXTYkkwB/w/wSeAk4DnALwPPAH4WeFOSbwb+APhEVb1l6PR/B/xn4DjgE8Dbm/c8BngP8H8BxwK/D7wnybFD574Y+F+AbwZ2AL/anHtSc+5vAMc05X+e5PiVzgWe1bx+U1UdWVX/OOZX3gc8rXnvdwDvTHLEih0laeIMOJLW4hnA8VX12qp6uKo+D7wJeGFVvQ94J/AB4IeBnxs59z1V9eGqegi4EPj+JCcDPwLcWFVvrarZqvoz4L8DPzp07n+tqhuq6uvAVQxCB8BLgGuq6pqq6lfV+4H9zfVXOndFVfW2qrq3adfvATuBb1/t+ZImx4AjaS2eCDy+mQ66P8n9wK8BJzTHLwWeCvxpVd07cu6t8xtV9RXgPuDxzc8tI3VvYTBCNO+Ooe2vAUcOtednRtrzTODEVZy7oiS/muRzzdTZ/cDjGIxASdpkpleuIklLuhX4l6o6dfRAkh6DgPMW4BeS/NeqummoyslDdY9kMO3zr83PE1lsF/D/rrI9b62q//VR/RYDtdzBZr3N/8FgGu66quon+SKQw7iWpA3mCI6ktfhvwANJXpXkG5L0kjw1yTMYjOQUg7U4vwO8pQk98344yTOT7GCwFuejVXUrcA3wbUlenGQ6yQuApwB/tYr2vA340ST/c9OWI5KcnuQJqzj3bqAPPGmJ40cBs0296SSvBh67iveV1AIDjqTDVlVzwPMZrGP5F+Ae4E+AHwR+BXhpU+e3GYSdC4ZOfwfwGgZTU9/DYP0MzVTW84H/HbiXwajJ86vqnlW051bgLAbh6m4GIzr/kVX8W1dVXwNeB3ykmd76vpEq72UwinQDgymzBxmaZpO0uaRq2VFZSVp3Sf4UOFBVv952WyR1kyM4kiSpcww4kiSpc5yikiRJneMIjiRJ6pzOPQfnuOOOq927d0/0ml+454GJXk+SJA3cfcsN91TV8aPlnQs4u3fvZv/+/RO95isu+8BErydJkgbe+LIzRp98DjhFJUmSOsiAI0mSOseAI0mSOseAI0mSOseAI0mSOseAI0mSOseAI0mSOseAI0mSOseAI0mSOseAI0mSOseAI0mSOqdz30UlSZJaUEWvqu1WLDDgSJK03VUx3S92zM01P/2h7YP7Ow85trher4V88/olyg04kiRtYVP9/iGhY6Y/HD76zMzNsbN5PTSYDLa7tmal1YCT5Ezgj4Ae8CdVdfHI8V8BXg7MAncDP1tVY78WXZKkLaOKmf4gWMzMHQwoi8sO7u9YCCeHlm+maaHNpLWAk6THYGTpucABYF+SvVX12aFq/wTsqaqvJXkFcAnwgsm3VpK03U31i5n+IHwMB5FxoWSp7YV6/X7bv07ntTmCcxpwU1V9HiDJFcBZwELAqaoPDdX/KPCSibZQkrRlTfX7CwFj8DoY8ZhupnSWCiszQ8eG6007UrKltBlwTgJuHdo/AHzvMvVfBvz1uANJzgPOA9i1a9d6tU+SNAlVTDVTNuNCxkx/EEqGg8pydedfnbrZ3rbEIuMkLwH2AM8ed7yqLgUuBdizZ493tCRthOZjwDPNKMh8mJjfnl4IHzW0PRpOFp87ON7Op2/UbW0GnNuAk4f2n9CULZLkDOBC4NlV9dCE2iZJW1Pzcd/FoWPwupqy0eOj+137pI26q82Asw84NckpDILNC4EXD1dI8nTgj4Ezq+quyTdRktbXVBM+Rn8GQWLxscUhpMaWD4eQ+XqSWgw4VTWb5HzgvQw+Jn5ZVV2X5LXA/qraC/wOcCTwziQAX6iqH2urzZK6KzUcLkZDyEr7qy9zKkaajFbX4FTVNcA1I2WvHto+Y+KNkrQ5NOs9hoNCb3i7xpdPN4tLR0PGaJ3pfjXvYfCQumhLLDKW1K5U0RsKBL2FkFFj90fLlg4mh4aShTI/ASNpDQw40haRoaDQq34TOA6Ggvng0BsJC/N1D4aI1dQZeU/DhqQtxoAjraSZKumNBIvewujEoYFjalFIWKrufCAZ/76j4cNPr0jS6hlwtLlUMVUwVYv/6E8tCgB1yPH5IDC1sD0aKkbfY/Gx3kIoGX0f12ZI0lZkwNmOaj4g1KJAMLWw3190bL7ufAgYPW++/GC95ULJ4vcZF1bSdv9IkrY8A856qdGQUIv+6I+WTY2tf2iwWOo9xl1rYSpjhXNcTyFJ6joDzhrd9f79nLf/U66PkCRpE/Hv8lqViz8lSdps/NssSZI6x4AjSZI6x4AjSZI6x4AjSZI6x4AjSZI6p9WAk+TMJNcnuSnJBWOO70xyZXP8Y0l2t9BMSZK0xbQWcJL0gNcDzwOeArwoyVNGqr0M+GJVfSvwB8BvT7aVkiRpK1ox4CT5pSRHb8C1TwNuqqrPV9XDwBXAWSN1zgIub7avBp6TxCf5S5KkZa1mBOcEYF+Sq5oppfUKGCcBtw7tH2jKxtapqlngS8Cx63R9SZLUUSsGnKr6deBU4M3AucCNSX4zyZM3uG2rluS8JPuT7L/77rvbbo4kSWrZqtbgVFUBdzQ/s8DRwNVJLlnDtW8DTh7af0JTNrZOkmngccC9Y9p3aVXtqao9xx9//BqaJEmSumA1a3BemeRa4BLgI8B3VtUrgO8BfmoN194HnJrklCQ7gBcCe0fq7AXOabZ/GvhgE7YkSZKWtJpvEz8G+MmqumW4sKr6SZ5/uBeuqtkk5wPvBXrAZVV1XZLXAvurai+DabG3JrkJuI9BCJIkSVrWigGnql6zzLHPreXiVXUNcM1I2auHth8EfmYt15AkSduPTzKWJEmdY8CRJEmdY8CRJEmdY8CRJEmdY8BZq6kp5vzyCEmSNpXVfExcy/jmM76H13zhfqhiqvnpzb/2Dy1bTfmgrL+o3rj3PXS/P/b4aFnPRwlJkjrOgLNeEvoJfQaPet7URkLToYGpf8ix4f1xQWqw3V/y/XojQWzwPgfD16JQ13b/SJK2PAPOdrTJw1jGBKbemODVGwpP44PU4rLRYDXd748NXaN1HfGSpK3HgKNNpxJme2HTLBEbGc2aXghN48PTcPl0E8oOvq6u7vD7TvebcNd2P0jSFmLAkVaSMJcw13LCyJjQMx+ShoPTaACbr7s4XPXHlk+Pec/pvqNYkrYeA460RcyPbM0yxUMTv3gtDjwL4Wk4GA2OLS6b318cmIbLppd4r5m+oUrS4TPgSFpZG+FqaB3VfBCaHh1ZGiqbPz4flhbqjqkztp6jVFKnGHAkbU4LU4NTPDyJy40EnvlRppmhkaXR4/M/M4ccP7SOQUqarFYCTpJjgCuB3cDNwNlV9cWROk8D3gA8FpgDXldVV060oZK2jUp4pNfjkV5vQ68ztTAC1Wdm7tAwNDMcnOaWDlHDx4bPc2pPGmhrBOcC4ANVdXGSC5r9V43U+Rrw0qq6McnjgWuTvLeq7p9wWyVp3fSnQp9BkPr6zAZcoFkvtVRYWjpAHVpnZu7Q+n6aT1tFWwHnLOD0Zvty4G8ZCThVdcPQ9r8muQs4Hrh/Ii2UpK1ofr1Ub4oH1/u9m3VRo+FnOBiN3547pHxxiJqj58CT1llbAeeEqrq92b4DOGG5yklOA3YA/7zRDZMkLWFoXdRD6/zXY2po5GjH3KHhaDgYzYeiQ8vnFr2H6522tw0LOEn+BviWMYcuHN6pqkqy5F2Y5ETgrcA5VdVfos55wHkAu3btOuw2S5La0Z+a4qGpwaf0vrpO7zk1HJjGBKMdc6MhabC/Y4l60wamLWXDAk5VnbHUsSR3Jjmxqm5vAsxdS9R7LPAe4MKq+ugy17oUuBRgz5493oGSJPpTUzw8NcXD6/SXbqpfTPfnhgLTSttzC6NRw9s7mpGmrE+ztIS2pqj2AucAFzev7x6tkGQH8C7gLVV19WSbJ0nSYv2p8PDU9PoEpqpmdGhxIJoPQDuGgtDBOouDkyNLy2sr4FwMXJXkZcAtwNkASfYAP19VL2/KngUcm+Tc5rxzq+oTk2+uJEnraNFjCdb2cbqpfv+Q4HPw9dDQtHh7/rzuLfRuJeBU1b3Ac8aU7wde3my/DXjbhJsmSdKW0p+a4sGpKR5cy5/05vECg1GjOXbOjgtD47d3Du1vpoXdPslYkqTtbujxAoc9olTFVNWmWVtkwJEkSWuX0M9miTf4UEpJktQ9BhxJktQ5BhxJktQ5BhxJktQ5BhxJktQ5BhxJktQ5BhxJktQ5BhxJktQ5BhxJktQ5BhxJktQ5BhxJktQ5BhxJktQ5qU301ebrIckDwPVtt2ObOA64p+1GbBP29WTYz5NjX0/GdujnJ1bV8aOFXfw28eurak/bjdgOkuy3ryfDvp4M+3ly7OvJ2M797BSVJEnqHAOOJEnqnC4GnEvbbsA2Yl9Pjn09Gfbz5NjXk7Ft+7lzi4wlSZK6OIIjSZK2OQOOJEnqnE4FnCRnJrk+yU1JLmi7PV2W5OYkn07yiST7225PVyS5LMldST4zVHZMkvcnubF5PbrNNnbFEn19UZLbmvv6E0l+uM02dkGSk5N8KMlnk1yX5JVNuff1Olumr7flfd2ZNThJesANwHOBA8A+4EVV9dlWG9ZRSW4G9lRV1x8gNVFJngV8BXhLVT21KbsEuK+qLm6C+9FV9ao229kFS/T1RcBXqup322xblyQ5ETixqj6e5CjgWuDHgXPxvl5Xy/T12WzD+7pLIzinATdV1eer6mHgCuCsltskPSpV9WHgvpHis4DLm+3LGfyDpTVaoq+1zqrq9qr6eLP9APA54CS8r9fdMn29LXUp4JwE3Dq0f4Bt/D/sBBTwviTXJjmv7cZ03AlVdXuzfQdwQpuN2QbOT/KpZgrLaZN1lGQ38HTgY3hfb6iRvoZteF93KeBosp5ZVd8NPA/4xWa4XxusBnPK3ZhX3pzeADwZeBpwO/B7rbamQ5IcCfw58MtV9eXhY97X62tMX2/L+7pLAec24OSh/Sc0ZdoAVXVb83oX8C4GU4TaGHc2c+vzc+x3tdyezqqqO6tqrqr6wJvwvl4XSWYY/MF9e1X9RVPsfb0BxvX1dr2vuxRw9gGnJjklyQ7ghcDeltvUSUke0yxgI8ljgB8CPrP8WVqDvcA5zfY5wLtbbEunzf/BbfwE3tdrliTAm4HPVdXvDx3yvl5nS/X1dr2vO/MpKoDmo29/CPSAy6rqde22qJuSPInBqA0MvpH+Hfb1+kjyZ8DpwHHAncBrgL8ErgJ2AbcAZ1eVi2PXaIm+Pp3BMH4BNwM/N7RORIchyTOBvwM+DfSb4l9jsDbE+3odLdPXL2Ib3tedCjiSJEnQrSkqSZIkwIAjSZI6yIAjSZI6x4AjSZI6x4AjSZI6x4AjSZI6x4AjSZI6x4AjactK8ozmCwSPaJ6wfV2Sp7bdLknt80F/kra0JL8BHAF8A3Cgqn6r5SZJ2gQMOJK2tOa75/YBDwI/UFVzLTdJ0ibgFJWkre5Y4EjgKAYjOZLkCI6krS3JXuAK4BTgxKo6v+UmSdoEpttugCQdriQvBR6pqnck6QH/kOQHq+qDbbdNUrscwZEkSZ3jGhxJktQ5BhxJktQ5BhxJktQ5BhxJktQ5BhxJktQ5BhxJktQ5BhxJktQ5/z820QnRkc/7pAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 576x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Structure visualization.\n",
    "fig, axs = plt.subplots(3, 1, tight_layout=True, figsize=(8, 6))\n",
    "for sim_s, tap_n, ax in zip(sim_tap.values(), sim_tap.keys(), axs):\n",
    "    sim_s.plot_structures(z=0, ax=ax)\n",
    "    ax.set_xlim(0, 2 * pad_x + taper_l)\n",
    "    ax.set_ylim(-1.5 * taper_w_out / 2, 1.5 * taper_w_out / 2)\n",
    "    ax.set_aspect(\"auto\")  # Used to better visualize the shapes.\n",
    "    ax.set_title(tap_n)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-18T17:42:45.927544Z",
     "iopub.status.busy": "2023-08-18T17:42:45.927354Z",
     "iopub.status.idle": "2023-08-18T17:42:46.349269Z",
     "shell.execute_reply": "2023-08-18T17:42:46.348760Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Simulation set up visualization.\n",
    "fig = plt.figure(tight_layout=True, figsize=(8, 6))\n",
    "gs = fig.add_gridspec(2, 2)\n",
    "ax1 = fig.add_subplot(gs[0, :])\n",
    "ax2 = fig.add_subplot(gs[1, 0])\n",
    "ax3 = fig.add_subplot(gs[1, 1])\n",
    "sim_tap[\"linear\"].plot(z=0, ax=ax1)\n",
    "sim_tap[\"linear\"].plot(x=pad_x, ax=ax2)\n",
    "sim_tap[\"linear\"].plot(x=pad_x + taper_l + pad_x / 2, ax=ax3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we will run the simulations using a batch object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-18T17:42:46.351442Z",
     "iopub.status.busy": "2023-08-18T17:42:46.351274Z",
     "iopub.status.idle": "2023-08-18T17:43:03.695170Z",
     "shell.execute_reply": "2023-08-18T17:43:03.692602Z"
    }
   },
   "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\">13:33:52 PDT </span>Created task <span style=\"color: #008000; text-decoration-color: #008000\">'linear'</span> with task_id                                 \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'fdve-4df5f2ac-f4db-4730-bf85-f72a3bbb3bb2v1'</span> and task_type <span style=\"color: #008000; text-decoration-color: #008000\">'FDTD'</span>.\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m13:33:52 PDT\u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'linear'\u001b[0m with task_id                                 \n",
       "\u001b[2;36m             \u001b[0m\u001b[32m'fdve-4df5f2ac-f4db-4730-bf85-f72a3bbb3bb2v1'\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 <a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-4df5f2ac-f4db-4730-bf85-f72a3bbb3bb2v1\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'https://tidy3d.simulation.cloud/workbenc</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-4df5f2ac-f4db-4730-bf85-f72a3bbb3bb2v1\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">h?taskId=fdve-4df5f2ac-f4db-4730-bf85-f72a3bbb3bb2v1'</span></a>.             \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mView task using web UI at \u001b]8;id=340341;https://tidy3d.simulation.cloud/workbench?taskId=fdve-4df5f2ac-f4db-4730-bf85-f72a3bbb3bb2v1\u001b\\\u001b[32m'https://tidy3d.simulation.cloud/workbenc\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=340341;https://tidy3d.simulation.cloud/workbench?taskId=fdve-4df5f2ac-f4db-4730-bf85-f72a3bbb3bb2v1\u001b\\\u001b[32mh?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=92644;https://tidy3d.simulation.cloud/workbench?taskId=fdve-4df5f2ac-f4db-4730-bf85-f72a3bbb3bb2v1\u001b\\\u001b[32mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=340341;https://tidy3d.simulation.cloud/workbench?taskId=fdve-4df5f2ac-f4db-4730-bf85-f72a3bbb3bb2v1\u001b\\\u001b[32m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=92644;https://tidy3d.simulation.cloud/workbench?taskId=fdve-4df5f2ac-f4db-4730-bf85-f72a3bbb3bb2v1\u001b\\\u001b[32mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=340341;https://tidy3d.simulation.cloud/workbench?taskId=fdve-4df5f2ac-f4db-4730-bf85-f72a3bbb3bb2v1\u001b\\\u001b[32m-4df5f2ac-f4db-4730-bf85-f72a3bbb3bb2v1'\u001b[0m\u001b]8;;\u001b\\.             \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7752c5842e384d2f9225820b18a4ea61",
       "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\">             </span>Created task <span style=\"color: #008000; text-decoration-color: #008000\">'quadratic'</span> with task_id                              \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'fdve-bad852b2-87cd-42ef-aabe-03ef55d1926dv1'</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'quadratic'\u001b[0m with task_id                              \n",
       "\u001b[2;36m             \u001b[0m\u001b[32m'fdve-bad852b2-87cd-42ef-aabe-03ef55d1926dv1'\u001b[0m and task_type \u001b[32m'FDTD'\u001b[0m.\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>View task using web UI at <a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-bad852b2-87cd-42ef-aabe-03ef55d1926dv1\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'https://tidy3d.simulation.cloud/workbenc</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-bad852b2-87cd-42ef-aabe-03ef55d1926dv1\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">h?taskId=fdve-bad852b2-87cd-42ef-aabe-03ef55d1926dv1'</span></a>.             \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mView task using web UI at \u001b]8;id=510030;https://tidy3d.simulation.cloud/workbench?taskId=fdve-bad852b2-87cd-42ef-aabe-03ef55d1926dv1\u001b\\\u001b[32m'https://tidy3d.simulation.cloud/workbenc\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=510030;https://tidy3d.simulation.cloud/workbench?taskId=fdve-bad852b2-87cd-42ef-aabe-03ef55d1926dv1\u001b\\\u001b[32mh?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=334394;https://tidy3d.simulation.cloud/workbench?taskId=fdve-bad852b2-87cd-42ef-aabe-03ef55d1926dv1\u001b\\\u001b[32mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=510030;https://tidy3d.simulation.cloud/workbench?taskId=fdve-bad852b2-87cd-42ef-aabe-03ef55d1926dv1\u001b\\\u001b[32m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=334394;https://tidy3d.simulation.cloud/workbench?taskId=fdve-bad852b2-87cd-42ef-aabe-03ef55d1926dv1\u001b\\\u001b[32mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=510030;https://tidy3d.simulation.cloud/workbench?taskId=fdve-bad852b2-87cd-42ef-aabe-03ef55d1926dv1\u001b\\\u001b[32m-bad852b2-87cd-42ef-aabe-03ef55d1926dv1'\u001b[0m\u001b]8;;\u001b\\.             \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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     },
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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\">13:33:53 PDT </span>Created task <span style=\"color: #008000; text-decoration-color: #008000\">'exponential'</span> with task_id                            \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'fdve-ecb0e9ab-17b8-498e-bed9-30fd185706f0v1'</span> and task_type <span style=\"color: #008000; text-decoration-color: #008000\">'FDTD'</span>.\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m13:33:53 PDT\u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'exponential'\u001b[0m with task_id                            \n",
       "\u001b[2;36m             \u001b[0m\u001b[32m'fdve-ecb0e9ab-17b8-498e-bed9-30fd185706f0v1'\u001b[0m and task_type \u001b[32m'FDTD'\u001b[0m.\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\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>View task using web UI at <a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-ecb0e9ab-17b8-498e-bed9-30fd185706f0v1\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'https://tidy3d.simulation.cloud/workbenc</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-ecb0e9ab-17b8-498e-bed9-30fd185706f0v1\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">h?taskId=fdve-ecb0e9ab-17b8-498e-bed9-30fd185706f0v1'</span></a>.             \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mView task using web UI at \u001b]8;id=179506;https://tidy3d.simulation.cloud/workbench?taskId=fdve-ecb0e9ab-17b8-498e-bed9-30fd185706f0v1\u001b\\\u001b[32m'https://tidy3d.simulation.cloud/workbenc\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=179506;https://tidy3d.simulation.cloud/workbench?taskId=fdve-ecb0e9ab-17b8-498e-bed9-30fd185706f0v1\u001b\\\u001b[32mh?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=829627;https://tidy3d.simulation.cloud/workbench?taskId=fdve-ecb0e9ab-17b8-498e-bed9-30fd185706f0v1\u001b\\\u001b[32mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=179506;https://tidy3d.simulation.cloud/workbench?taskId=fdve-ecb0e9ab-17b8-498e-bed9-30fd185706f0v1\u001b\\\u001b[32m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=829627;https://tidy3d.simulation.cloud/workbench?taskId=fdve-ecb0e9ab-17b8-498e-bed9-30fd185706f0v1\u001b\\\u001b[32mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=179506;https://tidy3d.simulation.cloud/workbench?taskId=fdve-ecb0e9ab-17b8-498e-bed9-30fd185706f0v1\u001b\\\u001b[32m-ecb0e9ab-17b8-498e-bed9-30fd185706f0v1'\u001b[0m\u001b]8;;\u001b\\.             \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>\n"
      ],
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       "\n"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Maximum FlexCredit cost for linear = 0.12\n",
      "Maximum FlexCredit cost for quadratic = 0.12\n",
      "Maximum FlexCredit cost for exponential = 0.12\n",
      "Maximum FlexCredit cost for batch = 0.36\n"
     ]
    }
   ],
   "source": [
    "# Initialize a simulation batch.\n",
    "batch = web.Batch(simulations=sim_tap, verbose=True)\n",
    "\n",
    "# Get the estimated simulation cost.\n",
    "tot_cost = 0\n",
    "for bat in batch.get_info().values():\n",
    "    sim_name = bat.taskName\n",
    "    cost = web.estimate_cost(bat.taskId, verbose=False)\n",
    "    tot_cost += cost\n",
    "    print(f\"Maximum FlexCredit cost for {sim_name} = {cost:.2f}\")\n",
    "print(f\"Maximum FlexCredit cost for batch = {tot_cost:.2f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run the batch and store all of the data in the `data_taper` dir."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-18T17:43:03.784022Z",
     "iopub.status.busy": "2023-08-18T17:43:03.783832Z",
     "iopub.status.idle": "2023-08-18T17:44:57.886255Z",
     "shell.execute_reply": "2023-08-18T17:44:57.884001Z"
    }
   },
   "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\">13:34:10 PDT </span>Started working on Batch.                                          \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m13:34:10 PDT\u001b[0m\u001b[2;36m \u001b[0mStarted working on Batch.                                          \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\">13:34:21 PDT </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.356</span> for the whole batch.                \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m13:34:21 PDT\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m0.356\u001b[0m for the whole batch.                \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>Use <span style=\"color: #008000; text-decoration-color: #008000\">'Batch.real_cost()'</span> to get the billed FlexCredit cost after the\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Batch has completed.                                               \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mUse \u001b[32m'Batch.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m to get the billed FlexCredit cost after the\n",
       "\u001b[2;36m             \u001b[0mBatch has completed.                                               \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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       "Output()"
      ]
     },
     "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\">13:34:58 PDT </span>Batch complete.                                                    \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m13:34:58 PDT\u001b[0m\u001b[2;36m \u001b[0mBatch complete.                                                    \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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     },
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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": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "batch_taper = batch.run(path_dir=\"data/data_taper\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Inverted taper results\n",
    "Now we will see the field intensity and the modal coupling efficiency for the 3 inverted tapers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
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     "shell.execute_reply": "2023-08-18T17:45:02.390250Z"
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   "outputs": [
    {
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       "Output()"
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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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       "<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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     },
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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>loading simulation from                                            \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>data/data_taper/fdve-<span style=\"color: #ffff00; text-decoration-color: #ffff00\">4df5f2ac-f4db-4730-bf85-f72a3bbb3bb2</span>v1.hdf5   \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mloading simulation from                                            \n",
       "\u001b[2;36m             \u001b[0mdata/data_taper/fdve-\u001b[93m4df5f2ac-f4db-4730-bf85-f72a3bbb3bb2\u001b[0mv1.hdf5   \n"
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       "Output()"
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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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       "<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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    },
    {
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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\">13:34:59 PDT </span>loading simulation from                                            \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>data/data_taper/fdve-<span style=\"color: #ffff00; text-decoration-color: #ffff00\">bad852b2-87cd-42ef-aabe-03ef55d1926d</span>v1.hdf5   \n",
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       "\u001b[2;36m13:34:59 PDT\u001b[0m\u001b[2;36m \u001b[0mloading simulation from                                            \n",
       "\u001b[2;36m             \u001b[0mdata/data_taper/fdve-\u001b[93mbad852b2-87cd-42ef-aabe-03ef55d1926d\u001b[0mv1.hdf5   \n"
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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\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">13:35:00 PDT </span>loading simulation from                                            \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>data/data_taper/fdve-<span style=\"color: #ffff00; text-decoration-color: #ffff00\">ecb0e9ab-17b8-498e-bed9-30fd185706f0</span>v1.hdf5   \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m13:35:00 PDT\u001b[0m\u001b[2;36m \u001b[0mloading simulation from                                            \n",
       "\u001b[2;36m             \u001b[0mdata/data_taper/fdve-\u001b[93mecb0e9ab-17b8-498e-bed9-30fd185706f0\u001b[0mv1.hdf5   \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sim_tap_results = dict(batch_taper.items())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-18T17:45:02.432934Z",
     "iopub.status.busy": "2023-08-18T17:45:02.432808Z",
     "iopub.status.idle": "2023-08-18T17:45:05.209200Z",
     "shell.execute_reply": "2023-08-18T17:45:05.208640Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x576 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(3, 1, tight_layout=True, figsize=(10, 8))\n",
    "for tap_n, sim_data, ax in zip(sim_tap_results.keys(), sim_tap_results.values(), axs):\n",
    "    sim_data.plot_field(\"field_xy\", \"Ey\", f=freq_c, val=\"abs\", ax=ax)\n",
    "    ax.set_title(tap_n)\n",
    "    ax.set_aspect(\"auto\")  # Used to better visualize the shapes.\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below, we have the $|E_{y}|$ field recorded by the monitors positioned at the taper tip and taper output of the quadratic structure. We can clearly visualize the Gaussian profile corresponding to the fields launched by a lensed fiber at the taper tip. In contrast, the output field corresponds to the transverse electric polarization of the fundamental output waveguide mode."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-18T17:45:05.211364Z",
     "iopub.status.busy": "2023-08-18T17:45:05.211192Z",
     "iopub.status.idle": "2023-08-18T17:45:05.952076Z",
     "shell.execute_reply": "2023-08-18T17:45:05.951538Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(1, 2, tight_layout=True, figsize=(8, 4))\n",
    "sim_tap_results[\"quadratic\"].plot_field(\"field_input\", \"Ey\", f=freq_c, val=\"abs\", ax=ax1)\n",
    "ax1.set_title(\"|Ey| at taper tip\")\n",
    "ax1.collections[-1].set_clim(0, 20)\n",
    "ax1.set_xlim(-2, 2)\n",
    "ax1.set_ylim(-2, 2)\n",
    "\n",
    "sim_tap_results[\"quadratic\"].plot_field(\"field_output\", \"Ey\", f=freq_c, val=\"abs\", ax=ax2)\n",
    "ax2.set_title(\"|Ey| at taper output\")\n",
    "ax2.collections[-1].set_clim(0, 50)\n",
    "ax2.set_xlim(-2, 2)\n",
    "ax2.set_ylim(-2, 2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The modal coupling efficiencies shown below reveal that the quadratic taper profile has the lowest insertion losses."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-18T17:45:05.954193Z",
     "iopub.status.busy": "2023-08-18T17:45:05.953989Z",
     "iopub.status.idle": "2023-08-18T17:45:06.100886Z",
     "shell.execute_reply": "2023-08-18T17:45:06.100244Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "power_tap_25 = []\n",
    "for sim_data in sim_tap_results.values():\n",
    "    mode_amps = sim_data[\"mode_0\"]\n",
    "    coeffs_f = mode_amps.amps.sel(direction=\"+\")\n",
    "    power_0 = np.abs(coeffs_f.sel(mode_index=0)) ** 2\n",
    "    power_0_db = 10 * np.log10(power_0)\n",
    "    power_tap_25.append(power_0_db)\n",
    "\n",
    "plot_colors = (\"black\", \"red\", \"blue\")\n",
    "fig, ax1 = plt.subplots(1, figsize=(8, 4))\n",
    "for data, color, label in zip(power_tap_25, plot_colors, sim_tap_results.keys()):\n",
    "    ax1.plot(\n",
    "        wl_range,\n",
    "        data,\n",
    "        color=color,\n",
    "        linestyle=\"solid\",\n",
    "        linewidth=1.0,\n",
    "        label=label,\n",
    "    )\n",
    "ax1.set_xlim([wl_range[0], wl_range[-1]])\n",
    "ax1.set_xlabel(\"Wavelength (um)\")\n",
    "ax1.set_ylabel(\"Power (dB)\")\n",
    "ax1.set_title(\"Coupling Efficiency\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Inverted Taper Length Sweep\n",
    "Before, we simulated the inverted tapers with a short length of only 25 $\\mu m$ to improve simulation time. Nevertheless, the designs can be improved by making the inverted taper larger in length so that its adiabaticity is increased. In the following sections, we increase the taper length to 50 $\\mu m$ and 100 $\\mu m$ (other values can be added to the sweep at a higher simulation cost)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-18T17:45:06.102813Z",
     "iopub.status.busy": "2023-08-18T17:45:06.102671Z",
     "iopub.status.idle": "2023-08-18T17:45:46.447894Z",
     "shell.execute_reply": "2023-08-18T17:45:46.445616Z"
    }
   },
   "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\">13:35:04 PDT </span><span style=\"color: #800000; text-decoration-color: #800000\">WARNING: Default value for the field monitor </span><span style=\"color: #008000; text-decoration-color: #008000\">'colocate'</span><span style=\"color: #800000; text-decoration-color: #800000\"> setting has</span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">changed to </span><span style=\"color: #008000; text-decoration-color: #008000\">'True'</span><span style=\"color: #800000; text-decoration-color: #800000\"> in Tidy3D </span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2.4</span><span style=\"color: #800000; text-decoration-color: #800000\">.</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span><span style=\"color: #800000; text-decoration-color: #800000\">. All field components will be    </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">colocated to the grid boundaries. Set to </span><span style=\"color: #008000; text-decoration-color: #008000\">'False'</span><span style=\"color: #800000; text-decoration-color: #800000\"> to get the raw    </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">fields on the Yee grid instead.                                    </span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m13:35:04 PDT\u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING: Default value for the field monitor \u001b[0m\u001b[32m'colocate'\u001b[0m\u001b[31m setting has\u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31mchanged to \u001b[0m\u001b[32m'True'\u001b[0m\u001b[31m in Tidy3D \u001b[0m\u001b[1;36m2.4\u001b[0m\u001b[31m.\u001b[0m\u001b[1;36m0\u001b[0m\u001b[31m. All field components will be    \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31mcolocated to the grid boundaries. Set to \u001b[0m\u001b[32m'False'\u001b[0m\u001b[31m to get the raw    \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31mfields on the Yee grid instead.                                    \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><span style=\"color: #800000; text-decoration-color: #800000\">WARNING: Default value for the field monitor </span><span style=\"color: #008000; text-decoration-color: #008000\">'colocate'</span><span style=\"color: #800000; text-decoration-color: #800000\"> setting has</span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">changed to </span><span style=\"color: #008000; text-decoration-color: #008000\">'True'</span><span style=\"color: #800000; text-decoration-color: #800000\"> in Tidy3D </span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2.4</span><span style=\"color: #800000; text-decoration-color: #800000\">.</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span><span style=\"color: #800000; text-decoration-color: #800000\">. All field components will be    </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">colocated to the grid boundaries. Set to </span><span style=\"color: #008000; text-decoration-color: #008000\">'False'</span><span style=\"color: #800000; text-decoration-color: #800000\"> to get the raw    </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">fields on the Yee grid instead.                                    </span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING: Default value for the field monitor \u001b[0m\u001b[32m'colocate'\u001b[0m\u001b[31m setting has\u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31mchanged to \u001b[0m\u001b[32m'True'\u001b[0m\u001b[31m in Tidy3D \u001b[0m\u001b[1;36m2.4\u001b[0m\u001b[31m.\u001b[0m\u001b[1;36m0\u001b[0m\u001b[31m. All field components will be    \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31mcolocated to the grid boundaries. Set to \u001b[0m\u001b[32m'False'\u001b[0m\u001b[31m to get the raw    \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31mfields on the Yee grid instead.                                    \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><span style=\"color: #800000; text-decoration-color: #800000\">WARNING: Default value for the field monitor </span><span style=\"color: #008000; text-decoration-color: #008000\">'colocate'</span><span style=\"color: #800000; text-decoration-color: #800000\"> setting has</span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">changed to </span><span style=\"color: #008000; text-decoration-color: #008000\">'True'</span><span style=\"color: #800000; text-decoration-color: #800000\"> in Tidy3D </span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2.4</span><span style=\"color: #800000; text-decoration-color: #800000\">.</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span><span style=\"color: #800000; text-decoration-color: #800000\">. All field components will be    </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">colocated to the grid boundaries. Set to </span><span style=\"color: #008000; text-decoration-color: #008000\">'False'</span><span style=\"color: #800000; text-decoration-color: #800000\"> to get the raw    </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">fields on the Yee grid instead.                                    </span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING: Default value for the field monitor \u001b[0m\u001b[32m'colocate'\u001b[0m\u001b[31m setting has\u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31mchanged to \u001b[0m\u001b[32m'True'\u001b[0m\u001b[31m in Tidy3D \u001b[0m\u001b[1;36m2.4\u001b[0m\u001b[31m.\u001b[0m\u001b[1;36m0\u001b[0m\u001b[31m. All field components will be    \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31mcolocated to the grid boundaries. Set to \u001b[0m\u001b[32m'False'\u001b[0m\u001b[31m to get the raw    \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31mfields on the Yee grid instead.                                    \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><span style=\"color: #800000; text-decoration-color: #800000\">WARNING: Default value for the field monitor </span><span style=\"color: #008000; text-decoration-color: #008000\">'colocate'</span><span style=\"color: #800000; text-decoration-color: #800000\"> setting has</span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">changed to </span><span style=\"color: #008000; text-decoration-color: #008000\">'True'</span><span style=\"color: #800000; text-decoration-color: #800000\"> in Tidy3D </span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2.4</span><span style=\"color: #800000; text-decoration-color: #800000\">.</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span><span style=\"color: #800000; text-decoration-color: #800000\">. All field components will be    </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">colocated to the grid boundaries. Set to </span><span style=\"color: #008000; text-decoration-color: #008000\">'False'</span><span style=\"color: #800000; text-decoration-color: #800000\"> to get the raw    </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">fields on the Yee grid instead.                                    </span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING: Default value for the field monitor \u001b[0m\u001b[32m'colocate'\u001b[0m\u001b[31m setting has\u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31mchanged to \u001b[0m\u001b[32m'True'\u001b[0m\u001b[31m in Tidy3D \u001b[0m\u001b[1;36m2.4\u001b[0m\u001b[31m.\u001b[0m\u001b[1;36m0\u001b[0m\u001b[31m. All field components will be    \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31mcolocated to the grid boundaries. Set to \u001b[0m\u001b[32m'False'\u001b[0m\u001b[31m to get the raw    \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31mfields on the Yee grid instead.                                    \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><span style=\"color: #800000; text-decoration-color: #800000\">WARNING: Default value for the field monitor </span><span style=\"color: #008000; text-decoration-color: #008000\">'colocate'</span><span style=\"color: #800000; text-decoration-color: #800000\"> setting has</span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">changed to </span><span style=\"color: #008000; text-decoration-color: #008000\">'True'</span><span style=\"color: #800000; text-decoration-color: #800000\"> in Tidy3D </span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2.4</span><span style=\"color: #800000; text-decoration-color: #800000\">.</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span><span style=\"color: #800000; text-decoration-color: #800000\">. All field components will be    </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">colocated to the grid boundaries. Set to </span><span style=\"color: #008000; text-decoration-color: #008000\">'False'</span><span style=\"color: #800000; text-decoration-color: #800000\"> to get the raw    </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">fields on the Yee grid instead.                                    </span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING: Default value for the field monitor \u001b[0m\u001b[32m'colocate'\u001b[0m\u001b[31m setting has\u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31mchanged to \u001b[0m\u001b[32m'True'\u001b[0m\u001b[31m in Tidy3D \u001b[0m\u001b[1;36m2.4\u001b[0m\u001b[31m.\u001b[0m\u001b[1;36m0\u001b[0m\u001b[31m. All field components will be    \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31mcolocated to the grid boundaries. Set to \u001b[0m\u001b[32m'False'\u001b[0m\u001b[31m to get the raw    \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31mfields on the Yee grid instead.                                    \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><span style=\"color: #800000; text-decoration-color: #800000\">WARNING: Default value for the field monitor </span><span style=\"color: #008000; text-decoration-color: #008000\">'colocate'</span><span style=\"color: #800000; text-decoration-color: #800000\"> setting has</span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">changed to </span><span style=\"color: #008000; text-decoration-color: #008000\">'True'</span><span style=\"color: #800000; text-decoration-color: #800000\"> in Tidy3D </span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2.4</span><span style=\"color: #800000; text-decoration-color: #800000\">.</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span><span style=\"color: #800000; text-decoration-color: #800000\">. All field components will be    </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">colocated to the grid boundaries. Set to </span><span style=\"color: #008000; text-decoration-color: #008000\">'False'</span><span style=\"color: #800000; text-decoration-color: #800000\"> to get the raw    </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">fields on the Yee grid instead.                                    </span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING: Default value for the field monitor \u001b[0m\u001b[32m'colocate'\u001b[0m\u001b[31m setting has\u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31mchanged to \u001b[0m\u001b[32m'True'\u001b[0m\u001b[31m in Tidy3D \u001b[0m\u001b[1;36m2.4\u001b[0m\u001b[31m.\u001b[0m\u001b[1;36m0\u001b[0m\u001b[31m. All field components will be    \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31mcolocated to the grid boundaries. Set to \u001b[0m\u001b[32m'False'\u001b[0m\u001b[31m to get the raw    \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31mfields on the Yee grid instead.                                    \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>Created task <span style=\"color: #008000; text-decoration-color: #008000\">'sim_50_linear'</span> with task_id                          \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'fdve-93460a36-f53c-44bb-af00-62758a847217v1'</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'sim_50_linear'\u001b[0m with task_id                          \n",
       "\u001b[2;36m             \u001b[0m\u001b[32m'fdve-93460a36-f53c-44bb-af00-62758a847217v1'\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 <a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-93460a36-f53c-44bb-af00-62758a847217v1\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'https://tidy3d.simulation.cloud/workbenc</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-93460a36-f53c-44bb-af00-62758a847217v1\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">h?taskId=fdve-93460a36-f53c-44bb-af00-62758a847217v1'</span></a>.             \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mView task using web UI at \u001b]8;id=506401;https://tidy3d.simulation.cloud/workbench?taskId=fdve-93460a36-f53c-44bb-af00-62758a847217v1\u001b\\\u001b[32m'https://tidy3d.simulation.cloud/workbenc\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=506401;https://tidy3d.simulation.cloud/workbench?taskId=fdve-93460a36-f53c-44bb-af00-62758a847217v1\u001b\\\u001b[32mh?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=670311;https://tidy3d.simulation.cloud/workbench?taskId=fdve-93460a36-f53c-44bb-af00-62758a847217v1\u001b\\\u001b[32mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=506401;https://tidy3d.simulation.cloud/workbench?taskId=fdve-93460a36-f53c-44bb-af00-62758a847217v1\u001b\\\u001b[32m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=670311;https://tidy3d.simulation.cloud/workbench?taskId=fdve-93460a36-f53c-44bb-af00-62758a847217v1\u001b\\\u001b[32mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=506401;https://tidy3d.simulation.cloud/workbench?taskId=fdve-93460a36-f53c-44bb-af00-62758a847217v1\u001b\\\u001b[32m-93460a36-f53c-44bb-af00-62758a847217v1'\u001b[0m\u001b]8;;\u001b\\.             \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e6f8bd420db54e2b86b431822586aef8",
       "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\">13:35:05 PDT </span>Created task <span style=\"color: #008000; text-decoration-color: #008000\">'sim_50_quadratic'</span> with task_id                       \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'fdve-2ad824f0-912a-4d65-8f7d-94cd382870c1v1'</span> and task_type <span style=\"color: #008000; text-decoration-color: #008000\">'FDTD'</span>.\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m13:35:05 PDT\u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'sim_50_quadratic'\u001b[0m with task_id                       \n",
       "\u001b[2;36m             \u001b[0m\u001b[32m'fdve-2ad824f0-912a-4d65-8f7d-94cd382870c1v1'\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 <a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-2ad824f0-912a-4d65-8f7d-94cd382870c1v1\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'https://tidy3d.simulation.cloud/workbenc</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-2ad824f0-912a-4d65-8f7d-94cd382870c1v1\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">h?taskId=fdve-2ad824f0-912a-4d65-8f7d-94cd382870c1v1'</span></a>.             \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mView task using web UI at \u001b]8;id=828364;https://tidy3d.simulation.cloud/workbench?taskId=fdve-2ad824f0-912a-4d65-8f7d-94cd382870c1v1\u001b\\\u001b[32m'https://tidy3d.simulation.cloud/workbenc\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=828364;https://tidy3d.simulation.cloud/workbench?taskId=fdve-2ad824f0-912a-4d65-8f7d-94cd382870c1v1\u001b\\\u001b[32mh?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=945126;https://tidy3d.simulation.cloud/workbench?taskId=fdve-2ad824f0-912a-4d65-8f7d-94cd382870c1v1\u001b\\\u001b[32mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=828364;https://tidy3d.simulation.cloud/workbench?taskId=fdve-2ad824f0-912a-4d65-8f7d-94cd382870c1v1\u001b\\\u001b[32m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=945126;https://tidy3d.simulation.cloud/workbench?taskId=fdve-2ad824f0-912a-4d65-8f7d-94cd382870c1v1\u001b\\\u001b[32mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=828364;https://tidy3d.simulation.cloud/workbench?taskId=fdve-2ad824f0-912a-4d65-8f7d-94cd382870c1v1\u001b\\\u001b[32m-2ad824f0-912a-4d65-8f7d-94cd382870c1v1'\u001b[0m\u001b]8;;\u001b\\.             \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "38bf940ed67b4f41905cebde02ada27b",
       "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\">             </span>Created task <span style=\"color: #008000; text-decoration-color: #008000\">'sim_50_exponential'</span> with task_id                     \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'fdve-33777f58-6d7c-4b17-8e1d-d8aaa87419f2v1'</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'sim_50_exponential'\u001b[0m with task_id                     \n",
       "\u001b[2;36m             \u001b[0m\u001b[32m'fdve-33777f58-6d7c-4b17-8e1d-d8aaa87419f2v1'\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 <a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-33777f58-6d7c-4b17-8e1d-d8aaa87419f2v1\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'https://tidy3d.simulation.cloud/workbenc</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-33777f58-6d7c-4b17-8e1d-d8aaa87419f2v1\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">h?taskId=fdve-33777f58-6d7c-4b17-8e1d-d8aaa87419f2v1'</span></a>.             \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mView task using web UI at \u001b]8;id=467091;https://tidy3d.simulation.cloud/workbench?taskId=fdve-33777f58-6d7c-4b17-8e1d-d8aaa87419f2v1\u001b\\\u001b[32m'https://tidy3d.simulation.cloud/workbenc\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=467091;https://tidy3d.simulation.cloud/workbench?taskId=fdve-33777f58-6d7c-4b17-8e1d-d8aaa87419f2v1\u001b\\\u001b[32mh?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=395418;https://tidy3d.simulation.cloud/workbench?taskId=fdve-33777f58-6d7c-4b17-8e1d-d8aaa87419f2v1\u001b\\\u001b[32mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=467091;https://tidy3d.simulation.cloud/workbench?taskId=fdve-33777f58-6d7c-4b17-8e1d-d8aaa87419f2v1\u001b\\\u001b[32m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=395418;https://tidy3d.simulation.cloud/workbench?taskId=fdve-33777f58-6d7c-4b17-8e1d-d8aaa87419f2v1\u001b\\\u001b[32mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=467091;https://tidy3d.simulation.cloud/workbench?taskId=fdve-33777f58-6d7c-4b17-8e1d-d8aaa87419f2v1\u001b\\\u001b[32m-33777f58-6d7c-4b17-8e1d-d8aaa87419f2v1'\u001b[0m\u001b]8;;\u001b\\.             \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "332230540a8b42a4825fa78ce0b1dddb",
       "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\">13:35:06 PDT </span>Created task <span style=\"color: #008000; text-decoration-color: #008000\">'sim_100_linear'</span> with task_id                         \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'fdve-59ab670e-e863-4c16-aaaf-68c7e5ffd5d7v1'</span> and task_type <span style=\"color: #008000; text-decoration-color: #008000\">'FDTD'</span>.\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m13:35:06 PDT\u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'sim_100_linear'\u001b[0m with task_id                         \n",
       "\u001b[2;36m             \u001b[0m\u001b[32m'fdve-59ab670e-e863-4c16-aaaf-68c7e5ffd5d7v1'\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 <a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-59ab670e-e863-4c16-aaaf-68c7e5ffd5d7v1\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'https://tidy3d.simulation.cloud/workbenc</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-59ab670e-e863-4c16-aaaf-68c7e5ffd5d7v1\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">h?taskId=fdve-59ab670e-e863-4c16-aaaf-68c7e5ffd5d7v1'</span></a>.             \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mView task using web UI at \u001b]8;id=309046;https://tidy3d.simulation.cloud/workbench?taskId=fdve-59ab670e-e863-4c16-aaaf-68c7e5ffd5d7v1\u001b\\\u001b[32m'https://tidy3d.simulation.cloud/workbenc\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=309046;https://tidy3d.simulation.cloud/workbench?taskId=fdve-59ab670e-e863-4c16-aaaf-68c7e5ffd5d7v1\u001b\\\u001b[32mh?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=570336;https://tidy3d.simulation.cloud/workbench?taskId=fdve-59ab670e-e863-4c16-aaaf-68c7e5ffd5d7v1\u001b\\\u001b[32mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=309046;https://tidy3d.simulation.cloud/workbench?taskId=fdve-59ab670e-e863-4c16-aaaf-68c7e5ffd5d7v1\u001b\\\u001b[32m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=570336;https://tidy3d.simulation.cloud/workbench?taskId=fdve-59ab670e-e863-4c16-aaaf-68c7e5ffd5d7v1\u001b\\\u001b[32mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=309046;https://tidy3d.simulation.cloud/workbench?taskId=fdve-59ab670e-e863-4c16-aaaf-68c7e5ffd5d7v1\u001b\\\u001b[32m-59ab670e-e863-4c16-aaaf-68c7e5ffd5d7v1'\u001b[0m\u001b]8;;\u001b\\.             \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "41d5d7b89b1c44fdb8fd8efc28f240ee",
       "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\">             </span>Created task <span style=\"color: #008000; text-decoration-color: #008000\">'sim_100_quadratic'</span> with task_id                      \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'fdve-ed1f7ff1-c31f-4ca1-8049-1a5bb827fe2av1'</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'sim_100_quadratic'\u001b[0m with task_id                      \n",
       "\u001b[2;36m             \u001b[0m\u001b[32m'fdve-ed1f7ff1-c31f-4ca1-8049-1a5bb827fe2av1'\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 <a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-ed1f7ff1-c31f-4ca1-8049-1a5bb827fe2av1\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'https://tidy3d.simulation.cloud/workbenc</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-ed1f7ff1-c31f-4ca1-8049-1a5bb827fe2av1\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">h?taskId=fdve-ed1f7ff1-c31f-4ca1-8049-1a5bb827fe2av1'</span></a>.             \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mView task using web UI at \u001b]8;id=34964;https://tidy3d.simulation.cloud/workbench?taskId=fdve-ed1f7ff1-c31f-4ca1-8049-1a5bb827fe2av1\u001b\\\u001b[32m'https://tidy3d.simulation.cloud/workbenc\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=34964;https://tidy3d.simulation.cloud/workbench?taskId=fdve-ed1f7ff1-c31f-4ca1-8049-1a5bb827fe2av1\u001b\\\u001b[32mh?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=796240;https://tidy3d.simulation.cloud/workbench?taskId=fdve-ed1f7ff1-c31f-4ca1-8049-1a5bb827fe2av1\u001b\\\u001b[32mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=34964;https://tidy3d.simulation.cloud/workbench?taskId=fdve-ed1f7ff1-c31f-4ca1-8049-1a5bb827fe2av1\u001b\\\u001b[32m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=796240;https://tidy3d.simulation.cloud/workbench?taskId=fdve-ed1f7ff1-c31f-4ca1-8049-1a5bb827fe2av1\u001b\\\u001b[32mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=34964;https://tidy3d.simulation.cloud/workbench?taskId=fdve-ed1f7ff1-c31f-4ca1-8049-1a5bb827fe2av1\u001b\\\u001b[32m-ed1f7ff1-c31f-4ca1-8049-1a5bb827fe2av1'\u001b[0m\u001b]8;;\u001b\\.             \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "395b063ae85d482d99c7a8546e92aaea",
       "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\">13:35:07 PDT </span>Created task <span style=\"color: #008000; text-decoration-color: #008000\">'sim_100_exponential'</span> with task_id                    \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'fdve-46c356b0-f5a1-4cfa-9eb0-f5f697b7a27dv1'</span> and task_type <span style=\"color: #008000; text-decoration-color: #008000\">'FDTD'</span>.\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m13:35:07 PDT\u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'sim_100_exponential'\u001b[0m with task_id                    \n",
       "\u001b[2;36m             \u001b[0m\u001b[32m'fdve-46c356b0-f5a1-4cfa-9eb0-f5f697b7a27dv1'\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 <a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-46c356b0-f5a1-4cfa-9eb0-f5f697b7a27dv1\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'https://tidy3d.simulation.cloud/workbenc</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-46c356b0-f5a1-4cfa-9eb0-f5f697b7a27dv1\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">h?taskId=fdve-46c356b0-f5a1-4cfa-9eb0-f5f697b7a27dv1'</span></a>.             \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mView task using web UI at \u001b]8;id=696740;https://tidy3d.simulation.cloud/workbench?taskId=fdve-46c356b0-f5a1-4cfa-9eb0-f5f697b7a27dv1\u001b\\\u001b[32m'https://tidy3d.simulation.cloud/workbenc\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=696740;https://tidy3d.simulation.cloud/workbench?taskId=fdve-46c356b0-f5a1-4cfa-9eb0-f5f697b7a27dv1\u001b\\\u001b[32mh?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=870261;https://tidy3d.simulation.cloud/workbench?taskId=fdve-46c356b0-f5a1-4cfa-9eb0-f5f697b7a27dv1\u001b\\\u001b[32mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=696740;https://tidy3d.simulation.cloud/workbench?taskId=fdve-46c356b0-f5a1-4cfa-9eb0-f5f697b7a27dv1\u001b\\\u001b[32m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=870261;https://tidy3d.simulation.cloud/workbench?taskId=fdve-46c356b0-f5a1-4cfa-9eb0-f5f697b7a27dv1\u001b\\\u001b[32mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=696740;https://tidy3d.simulation.cloud/workbench?taskId=fdve-46c356b0-f5a1-4cfa-9eb0-f5f697b7a27dv1\u001b\\\u001b[32m-46c356b0-f5a1-4cfa-9eb0-f5f697b7a27dv1'\u001b[0m\u001b]8;;\u001b\\.             \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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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\"></pre>\n"
      ],
      "text/plain": []
     },
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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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Maximum FlexCredit cost for sim_50_linear = 0.22\n",
      "Maximum FlexCredit cost for sim_50_quadratic = 0.22\n",
      "Maximum FlexCredit cost for sim_50_exponential = 0.22\n",
      "Maximum FlexCredit cost for sim_100_linear = 0.42\n",
      "Maximum FlexCredit cost for sim_100_quadratic = 0.42\n",
      "Maximum FlexCredit cost for sim_100_exponential = 0.42\n",
      "Maximum FlexCredit cost for batch = 1.94\n"
     ]
    }
   ],
   "source": [
    "# Taper lengths on the sweep.\n",
    "taper_sweep = [50, 100]\n",
    "sim_sweep = [get_simulations(tap_length=t_l) for t_l in taper_sweep]\n",
    "\n",
    "# Make a dictionary of {task name : simulation}\n",
    "sims = {\n",
    "    f\"sim_{tap_l}_{sim_name}\": sim\n",
    "    for sim_l, tap_l in zip(sim_sweep, taper_sweep)\n",
    "    for sim, sim_name in zip(sim_l.values(), sim_l.keys())\n",
    "}\n",
    "# Initialize a batch and run them all\n",
    "batch = web.Batch(simulations=sims, verbose=True)\n",
    "\n",
    "# Get the estimated simulation cost.\n",
    "tot_cost = 0\n",
    "for bat in batch.get_info().values():\n",
    "    sim_name = bat.taskName\n",
    "    cost = web.estimate_cost(bat.taskId, verbose=False)\n",
    "    tot_cost += cost\n",
    "    print(f\"Maximum FlexCredit cost for {sim_name} = {cost:.2f}\")\n",
    "print(f\"Maximum FlexCredit cost for batch = {tot_cost:.2f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run the batch and store all of the data in the `data_sweep` dir."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-18T17:45:46.559458Z",
     "iopub.status.busy": "2023-08-18T17:45:46.559325Z",
     "iopub.status.idle": "2023-08-18T17:47:08.196285Z",
     "shell.execute_reply": "2023-08-18T17:47:08.193958Z"
    }
   },
   "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\">13:35:40 PDT </span>Started working on Batch.                                          \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m13:35:40 PDT\u001b[0m\u001b[2;36m \u001b[0mStarted working on Batch.                                          \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\">13:36:19 PDT </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1.937</span> for the whole batch.                \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m13:36:19 PDT\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m1.937\u001b[0m for the whole batch.                \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>Use <span style=\"color: #008000; text-decoration-color: #008000\">'Batch.real_cost()'</span> to get the billed FlexCredit cost after the\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Batch has completed.                                               \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mUse \u001b[32m'Batch.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m to get the billed FlexCredit cost after the\n",
       "\u001b[2;36m             \u001b[0mBatch has completed.                                               \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6d11e6641808439c801d475783e99af4",
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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\">13:36:54 PDT </span>Batch complete.                                                    \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m13:36:54 PDT\u001b[0m\u001b[2;36m \u001b[0mBatch complete.                                                    \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": {},
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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"
    }
   ],
   "source": [
    "batch_sweep = batch.run(path_dir=\"data/data_sweep\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Print the simulation log corresponding to the longest inverted taper to inspection."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-18T17:47:08.836311Z",
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     "shell.execute_reply": "2023-08-18T17:47:09.842095Z"
    }
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   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5efca2d756c1440eafd560396fcbb171",
       "version_major": 2,
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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\"></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\">13:36:57 PDT </span>loading simulation from                                            \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>data/data_sweep/fdve-<span style=\"color: #ffff00; text-decoration-color: #ffff00\">ed1f7ff1-c31f-4ca1-8049-1a5bb827fe2a</span>v1.hdf5   \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m13:36:57 PDT\u001b[0m\u001b[2;36m \u001b[0mloading simulation from                                            \n",
       "\u001b[2;36m             \u001b[0mdata/data_sweep/fdve-\u001b[93med1f7ff1-c31f-4ca1-8049-1a5bb827fe2a\u001b[0mv1.hdf5   \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Simulation domain Nx, Ny, Nz: [3477, 126, 136]\n",
      "Applied symmetries: (0, -1, 1)\n",
      "Number of computational grid points: 1.5829e+07.\n",
      "Using subpixel averaging: True\n",
      "Number of time steps: 8.9104e+04\n",
      "Automatic shutoff factor: 1.00e-05\n",
      "Time step (s): 5.3908e-17\n",
      "\n",
      "\n",
      "Compute source modes time (s):     0.4150\n",
      "Compute monitor modes time (s):    8.6281\n",
      "Rest of setup time (s):            9.8532\n",
      "\n",
      "Running solver for 89104 time steps...\n",
      "- Time step   2364 / time 1.27e-13s (  2 % done), field decay: 1.00e+00\n",
      "- Time step   3564 / time 1.92e-13s (  4 % done), field decay: 1.00e+00\n",
      "- Time step   7128 / time 3.84e-13s (  8 % done), field decay: 7.76e-01\n",
      "- Time step  10692 / time 5.76e-13s ( 12 % done), field decay: 5.40e-01\n",
      "- Time step  14256 / time 7.69e-13s ( 16 % done), field decay: 4.18e-01\n",
      "- Time step  17820 / time 9.61e-13s ( 20 % done), field decay: 3.55e-01\n",
      "- Time step  21384 / time 1.15e-12s ( 24 % done), field decay: 3.02e-01\n",
      "- Time step  24949 / time 1.34e-12s ( 28 % done), field decay: 1.31e-05\n",
      "- Time step  28513 / time 1.54e-12s ( 32 % done), field decay: 1.39e-07\n",
      "Field decay smaller than shutoff factor, exiting solver.\n",
      "\n",
      "Solver time (s):                   30.2616\n",
      "Data write time (s):               0.0695\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(batch_sweep[\"sim_100_quadratic\"].log)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Parameter sweep results\n",
    "\n",
    "Now, we will get the batch results and plot the modal coupling efficiency at the central wavelength as a function of the inverted taper length."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-18T17:47:09.855786Z",
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     "iopub.status.idle": "2023-08-18T17:47:25.034078Z",
     "shell.execute_reply": "2023-08-18T17:47:25.033530Z"
    }
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   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8f628859120c4934a26e27ee83e361df",
       "version_major": 2,
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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\"></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\">13:36:58 PDT </span>loading simulation from                                            \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>data/data_sweep/fdve-<span style=\"color: #ffff00; text-decoration-color: #ffff00\">93460a36-f53c-44bb-af00-62758a847217</span>v1.hdf5   \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m13:36:58 PDT\u001b[0m\u001b[2;36m \u001b[0mloading simulation from                                            \n",
       "\u001b[2;36m             \u001b[0mdata/data_sweep/fdve-\u001b[93m93460a36-f53c-44bb-af00-62758a847217\u001b[0mv1.hdf5   \n"
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       "Output()"
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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": {
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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": {},
     "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\">13:36:59 PDT </span>loading simulation from                                            \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>data/data_sweep/fdve-<span style=\"color: #ffff00; text-decoration-color: #ffff00\">2ad824f0-912a-4d65-8f7d-94cd382870c1</span>v1.hdf5   \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m13:36:59 PDT\u001b[0m\u001b[2;36m \u001b[0mloading simulation from                                            \n",
       "\u001b[2;36m             \u001b[0mdata/data_sweep/fdve-\u001b[93m2ad824f0-912a-4d65-8f7d-94cd382870c1\u001b[0mv1.hdf5   \n"
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       "Output()"
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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"
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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": {},
     "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\">13:37:01 PDT </span>loading simulation from                                            \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>data/data_sweep/fdve-<span style=\"color: #ffff00; text-decoration-color: #ffff00\">33777f58-6d7c-4b17-8e1d-d8aaa87419f2</span>v1.hdf5   \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m13:37:01 PDT\u001b[0m\u001b[2;36m \u001b[0mloading simulation from                                            \n",
       "\u001b[2;36m             \u001b[0mdata/data_sweep/fdve-\u001b[93m33777f58-6d7c-4b17-8e1d-d8aaa87419f2\u001b[0mv1.hdf5   \n"
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       "Output()"
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       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>data/data_sweep/fdve-<span style=\"color: #ffff00; text-decoration-color: #ffff00\">59ab670e-e863-4c16-aaaf-68c7e5ffd5d7</span>v1.hdf5   \n",
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       "\u001b[2;36m13:37:02 PDT\u001b[0m\u001b[2;36m \u001b[0mloading simulation from                                            \n",
       "\u001b[2;36m             \u001b[0mdata/data_sweep/fdve-\u001b[93m59ab670e-e863-4c16-aaaf-68c7e5ffd5d7\u001b[0mv1.hdf5   \n"
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       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mloading simulation from                                            \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\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">13:37:03 PDT </span>loading simulation from                                            \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>data/data_sweep/fdve-<span style=\"color: #ffff00; text-decoration-color: #ffff00\">46c356b0-f5a1-4cfa-9eb0-f5f697b7a27d</span>v1.hdf5   \n",
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       "\u001b[2;36m13:37:03 PDT\u001b[0m\u001b[2;36m \u001b[0mloading simulation from                                            \n",
       "\u001b[2;36m             \u001b[0mdata/data_sweep/fdve-\u001b[93m46c356b0-f5a1-4cfa-9eb0-f5f697b7a27d\u001b[0mv1.hdf5   \n"
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   ],
   "source": [
    "coup_eff_tl = np.zeros((len(tap_names), len(taper_sweep) + 1))\n",
    "for i in range(len(tap_names)):\n",
    "    coup_eff_tl[i, 0] = power_tap_25[i].sel(f=freq_c)\n",
    "\n",
    "sims = {\n",
    "    f\"sim_{tap_l}_{sim_name}\": sim\n",
    "    for sim_l, tap_l in zip(sim_sweep, taper_sweep)\n",
    "    for sim, sim_name in zip(sim_l.values(), sim_l.keys())\n",
    "}\n",
    "\n",
    "for j, tl in enumerate(taper_sweep):\n",
    "    for i, sn in enumerate(tap_names):\n",
    "        data_sim = batch_sweep[f\"sim_{tl}_{sn}\"]\n",
    "        mode_amps = data_sim[\"mode_0\"]\n",
    "        coeffs_f = mode_amps.amps.sel(direction=\"+\", f=freq_c)\n",
    "        power_0 = np.abs(coeffs_f.sel(mode_index=0)) ** 2\n",
    "        power_0_db = 10 * np.log10(power_0)\n",
    "        coup_eff_tl[i, j + 1] = power_0_db"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As shown below, the longer the taper, the higher the modal coupling efficiency, as is expected due to the increased adiabaticity. However, the quadratic shape profile seems more advantageous for this case as it achieved a steady state coupling loss of $\\lt$ 0.6 dB for a 50 $\\mu m$ taper length. In contrast, the exponential shape needs double this value, and an even longer taper length would be necessary for the linear profile to obtain similar coupling loss. Detailed discussions about the effects of taper profile over the coupling efficiency, bandwidth, footprint size, or taper/fiber misalignments can be found in the literature [[1](https://www.mdpi.com/2076-3417/10/4/1538),[2](https://opg.optica.org/prj/fulltext.cfm?uri=prj-7-2-201&id=404538),[3](https://www.sciencedirect.com/science/article/abs/pii/S0030401811006389)].   \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-18T17:47:25.109549Z",
     "iopub.status.busy": "2023-08-18T17:47:25.109296Z",
     "iopub.status.idle": "2023-08-18T17:47:25.369923Z",
     "shell.execute_reply": "2023-08-18T17:47:25.369416Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, figsize=(6, 3))\n",
    "lin_style = [\"ok\", \"sr\", \"hb\"]\n",
    "taper_lengths = [taper_l] + taper_sweep[:]\n",
    "for ce, ls, lab in zip(coup_eff_tl, lin_style, tap_names):\n",
    "    ax.plot(taper_lengths, ce, ls, label=lab)\n",
    "ax.set_xlim([taper_lengths[0] - 2, taper_lengths[-1] + 2])\n",
    "ax.set_xlabel(r\"Taper Length ($\\mu m$)\")\n",
    "ax.set_ylabel(\"Coupling Efficiency (dB)\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we will visualize the field distribution and the wavelength-dependent coupling coefficients for these longer inverted taper structures."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-18T17:47:25.371857Z",
     "iopub.status.busy": "2023-08-18T17:47:25.371711Z",
     "iopub.status.idle": "2023-08-18T17:47:32.914783Z",
     "shell.execute_reply": "2023-08-18T17:47:32.914234Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>data/data_sweep/fdve-<span style=\"color: #ffff00; text-decoration-color: #ffff00\">59ab670e-e863-4c16-aaaf-68c7e5ffd5d7</span>v1.hdf5   \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mloading simulation from                                            \n",
       "\u001b[2;36m             \u001b[0mdata/data_sweep/fdve-\u001b[93m59ab670e-e863-4c16-aaaf-68c7e5ffd5d7\u001b[0mv1.hdf5   \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
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       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>data/data_sweep/fdve-<span style=\"color: #ffff00; text-decoration-color: #ffff00\">2ad824f0-912a-4d65-8f7d-94cd382870c1</span>v1.hdf5   \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m13:37:04 PDT\u001b[0m\u001b[2;36m \u001b[0mloading simulation from                                            \n",
       "\u001b[2;36m             \u001b[0mdata/data_sweep/fdve-\u001b[93m2ad824f0-912a-4d65-8f7d-94cd382870c1\u001b[0mv1.hdf5   \n"
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     },
     "metadata": {},
     "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\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>loading simulation from                                            \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>data/data_sweep/fdve-<span style=\"color: #ffff00; text-decoration-color: #ffff00\">46c356b0-f5a1-4cfa-9eb0-f5f697b7a27d</span>v1.hdf5   \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mloading simulation from                                            \n",
       "\u001b[2;36m             \u001b[0mdata/data_sweep/fdve-\u001b[93m46c356b0-f5a1-4cfa-9eb0-f5f697b7a27d\u001b[0mv1.hdf5   \n"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 720x576 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "final_sim_data = [\n",
    "    batch_sweep[\"sim_100_linear\"],\n",
    "    batch_sweep[\"sim_50_quadratic\"],\n",
    "    batch_sweep[\"sim_100_exponential\"],\n",
    "]\n",
    "\n",
    "fig, axs = plt.subplots(3, 1, tight_layout=True, figsize=(10, 8))\n",
    "for sim_data, tap_n, ax in zip(final_sim_data, tap_names, axs):\n",
    "    sim_data.plot_field(\"field_xy\", \"Ey\", f=freq_c, val=\"abs\", ax=ax)\n",
    "    ax.set_title(tap_n)\n",
    "    ax.set_aspect(\"auto\")  # Used to better visualize the shapes.\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-18T17:47:32.916877Z",
     "iopub.status.busy": "2023-08-18T17:47:32.916649Z",
     "iopub.status.idle": "2023-08-18T17:47:33.061635Z",
     "shell.execute_reply": "2023-08-18T17:47:33.061086Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_colors = (\"black\", \"red\", \"blue\")\n",
    "fig, ax1 = plt.subplots(1, figsize=(8, 4))\n",
    "for sim_data, color, label in zip(final_sim_data, plot_colors, tap_names):\n",
    "    mode_amps = sim_data[\"mode_0\"]\n",
    "    coeffs_f = mode_amps.amps.sel(direction=\"+\")\n",
    "    power_0 = np.abs(coeffs_f.sel(mode_index=0)) ** 2\n",
    "    power_0_db = 10 * np.log10(power_0)\n",
    "\n",
    "    ax1.plot(\n",
    "        wl_range,\n",
    "        power_0_db,\n",
    "        color=color,\n",
    "        linestyle=\"solid\",\n",
    "        linewidth=1.0,\n",
    "        label=label,\n",
    "    )\n",
    "ax1.set_xlim([wl_range[0], wl_range[-1]])\n",
    "ax1.set_xlabel(\"Wavelength (um)\")\n",
    "ax1.set_ylabel(\"Power (dB)\")\n",
    "ax1.set_title(\"Coupling Efficiency\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "applications": [
   "Passive photonic integrated circuit components"
  ],
  "description": "This notebook demonstrates how to model an inverse taper edge coupler in Tidy3D FDTD.",
  "feature_image": "./img/EdgeCoupler.png",
  "features": [
   "Mode analysis"
  ],
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "keywords": "edge coupler, inverse taper, Tidy3D, FDTD",
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.0"
  },
  "nbdime-conflicts": {
   "local_diff": [
    {
     "diff": [
      {
       "diff": [
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         "diff": [
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           "key": 5,
           "op": "addrange",
           "valuelist": "2"
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          {
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           "length": 1,
           "op": "removerange"
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     "diff": [
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  "title": "Inverse Taper Edge Coupler Modeling in Tidy3D | Flexcompute",
  "vscode": {
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