DistributedBraggReflectorCavity.ipynb

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   "source": [
    "# Distributed Bragg reflector and cavity"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "A [distributed Bragg reflector](https://en.wikipedia.org/wiki/Distributed_Bragg_reflector) (DBR) is a multilayer structure consisting of alternating layers of high refractive index and low refractive index. When the thickness of each layer is close to a quarter of the medium wavelength, nearly perfect reflection occurs due to constructive interference of the reflected waves at each layer. DBR is commonly used at optical and UV wavelengths due to the fact that metallic reflectors have a high loss at high frequencies. Besides free space optics, similar concepts have also been applied to integrated photonics and fiber optics. Furthermore, high-Q cavities based on DBR structures are widely popular in lasers, filters, and sensors.\n",
    "\n",
    "Although Tidy3D uses highly optimized algorithms and hardware designed to perform large 3D simulations at ease, the computational efficiency can be improved exponentially if we can reduce the dimension of the model. The DBR structure has translational symmetry along the two in-plane directions. Thus, simulating a DBR is effectively a 1D problem. \n",
    "\n",
    "<img src=\"img/dbr_schematic.png\" width=600 alt=\"Schematic of the DBR and cavity\">\n",
    "\n",
    "For a relevant example, please see the [waveguide bragg gratings notebook](https://www.flexcompute.com/tidy3d/examples/notebooks/BraggGratings/).\n",
    "\n",
    "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. For more simulation examples, please visit our [examples page](https://www.flexcompute.com/tidy3d/examples/). 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": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import tidy3d as td\n",
    "import tidy3d.web as web"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0c8e122",
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   "source": [
    "## Simulating the Stopband of a DBR"
   ]
  },
  {
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   "source": [
    "### Simulation Setup "
   ]
  },
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   "source": [
    "The most common DBR is made of alternating layers of titanium dioxide ($TiO_2$) and silica ($SiO_2$). The target stopband of the DBR is around 630 nm. "
   ]
  },
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   "source": [
    "lda0 = 0.63  # central wavelength\n",
    "freq0 = td.C_0 / lda0  # central frequency\n",
    "freqs = freq0 * np.linspace(0.5, 1.5, 1001)  # frequency range of interest"
   ]
  },
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   "source": [
    "n_tio2 = 2.5  # refractive index of TiO2\n",
    "n_sio2 = 1.5  # refractive index of SiO2\n",
    "n_s = 1.5  # refractive index of the substrate material. It's set to SiO2 in this case\n",
    "inf_eff = 10  # effective infinity in this model"
   ]
  },
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   "source": [
    "The bandwidth of the stopband is given by \n",
    "\n",
    "$$\n",
    "\\frac{\\Delta f}{f_0} = \\frac{4}{\\pi} arcsin(\\frac{n_1-n_2}{n_1+n_2}),\n",
    "$$\n",
    "\n",
    "where $f_0$ is the central frequency, $n_1$ is the refractive index of the high index material, and $n_2$ is the refractive index of the low index material. We use the above equation to estimate the bandwidth of the DBR."
   ]
  },
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     "text": [
      "The normalized bandwidth of the reflection band is 0.32\n"
     ]
    }
   ],
   "source": [
    "df = 4 * np.arcsin((n_tio2 - n_sio2) / (n_tio2 + n_sio2)) / np.pi\n",
    "print(f\"The normalized bandwidth of the reflection band is {df:1.2f}\")"
   ]
  },
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   "cell_type": "markdown",
   "id": "7be52b05",
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   "source": [
    "Next, we construct a function that builds the DBR layers given four parameters: the refractive indices of the materials, the number of layer pairs, and the starting position of the lowest layer. This function will be handy for constructing the DBR as well as the cavity device in the next section. "
   ]
  },
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    "def build_layers(n_1, n_2, N, z_0):\n",
    "    # n_1 and n_2 are the refractive indices of the two materials\n",
    "    # N is the number of repeated pairs of low/high refractive index material\n",
    "    # z_0 is the z coordinate of the lowest layer\n",
    "\n",
    "    material_1 = td.Medium(permittivity=n_1**2)  # define the first material\n",
    "    material_2 = td.Medium(permittivity=n_2**2)  # define the second material\n",
    "    t_1 = lda0 / (4 * n_1)  # thickness of the first material\n",
    "    t_2 = lda0 / (4 * n_2)  # thicness of the second material\n",
    "    layers = []  # holder for all the layers\n",
    "\n",
    "    # building layers alternatively\n",
    "    for i in range(2 * N):\n",
    "        if i % 2 == 0:\n",
    "            layers.append(\n",
    "                td.Structure(\n",
    "                    geometry=td.Box.from_bounds(\n",
    "                        rmin=(-inf_eff, -inf_eff, z_0),\n",
    "                        rmax=(inf_eff, inf_eff, z_0 + t_1),\n",
    "                    ),\n",
    "                    medium=material_1,\n",
    "                )\n",
    "            )\n",
    "            z_0 = z_0 + t_1\n",
    "        else:\n",
    "            layers.append(\n",
    "                td.Structure(\n",
    "                    geometry=td.Box.from_bounds(\n",
    "                        rmin=(-inf_eff, -inf_eff, z_0),\n",
    "                        rmax=(inf_eff, inf_eff, z_0 + t_2),\n",
    "                    ),\n",
    "                    medium=material_2,\n",
    "                )\n",
    "            )\n",
    "            z_0 = z_0 + t_2\n",
    "\n",
    "    return layers"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00772287",
   "metadata": {},
   "source": [
    "We plan to perform a parameter sweep of $N$, the number of layer pairs. In order to do so, we define another function that takes $N$ as an argument and builds the simulation including structures, source, monitor, and so on."
   ]
  },
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   "source": [
    "def make_DBR(N):\n",
    "    # build the DBR layers using the previously defined function\n",
    "    DBR = build_layers(n_tio2, n_sio2, N, 0)\n",
    "\n",
    "    thickness = N * (lda0 / (4 * n_tio2) + lda0 / (4 * n_sio2))  # total thickness of the DBR layers\n",
    "\n",
    "    # build the substrate structure\n",
    "    sub = td.Structure(\n",
    "        geometry=td.Box.from_bounds(\n",
    "            rmin=(-inf_eff, -inf_eff, -inf_eff), rmax=(inf_eff, inf_eff, 0)\n",
    "        ),\n",
    "        medium=td.Medium(permittivity=n_s**2),\n",
    "    )\n",
    "\n",
    "    # the entire DBR structure includes the layers and the substrate\n",
    "    DBR.append(sub)\n",
    "\n",
    "    # create a plane wave excitation source\n",
    "    fwidth = 0.5 * freq0  # width of the frequency distribution\n",
    "    plane_wave = td.PlaneWave(\n",
    "        source_time=td.GaussianPulse(freq0=freq0, fwidth=fwidth),\n",
    "        size=(td.inf, td.inf, 0),\n",
    "        center=(0, 0, thickness + lda0 / 4),\n",
    "        direction=\"-\",\n",
    "        pol_angle=0,\n",
    "    )\n",
    "\n",
    "    # create a flux monitor to measure the reflectance\n",
    "    flux_monitor = td.FluxMonitor(\n",
    "        center=(0, 0, thickness + lda0 / 2),\n",
    "        size=(td.inf, td.inf, 0),\n",
    "        freqs=freqs,\n",
    "        name=\"R\",\n",
    "    )\n",
    "\n",
    "    Lz = thickness + 2 * lda0  # simulation domain size in z direction\n",
    "    run_time = 100 / fwidth  # simulation run time\n",
    "\n",
    "    sim = td.Simulation(\n",
    "        size=(0, 0, Lz),  # simulation domain sizes in x and y directions are set to 0\n",
    "        center=(0, 0, thickness / 2),\n",
    "        grid_spec=td.GridSpec.auto(min_steps_per_wvl=60, wavelength=lda0),\n",
    "        structures=DBR,\n",
    "        sources=[plane_wave],\n",
    "        monitors=[flux_monitor],\n",
    "        run_time=run_time,\n",
    "        boundary_spec=td.BoundarySpec(\n",
    "            x=td.Boundary.periodic(), y=td.Boundary.periodic(), z=td.Boundary.pml()\n",
    "        ),  # pml is applied in the z direction\n",
    "        shutoff=1e-7,\n",
    "    )  # early shutoff level is decreased to 1e-7 to increase the simulation accuracy\n",
    "    return sim"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "318abb79",
   "metadata": {},
   "source": [
    "To visualize the relationship between reflectance and the number of repeated pairs, we perform a parameter sweep. N is swept from 2 to 10 in a total of 5 simulations."
   ]
  },
  {
   "cell_type": "code",
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   "source": [
    "Ns = np.array([2, 3, 4, 5, 10])  # collection of N for the parameter sweep\n",
    "sims = {f\"N={N:.2f}\": make_DBR(N) for N in Ns}  # construct simulations for each N from Ns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6568d3d7",
   "metadata": {},
   "source": [
    "Submit the batch to the server. "
   ]
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      "\n",
      "KeyboardInterrupt\n",
      "\n"
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   ],
   "source": [
    "batch = web.Batch(simulations=sims, verbose=True)\n",
    "batch_results = batch.run(path_dir=\"data\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5fb6368",
   "metadata": {},
   "source": [
    "### Result Visualization "
   ]
  },
  {
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   "id": "e5624996",
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   "source": [
    "Once the batch of simulations is complete, we can plot the reflectance spectra.\n",
    "\n",
    "Analytically, the reflectance at the central frequency is given by\n",
    "\n",
    "$$\n",
    "R = [\\frac{n_0(n_1)^{2N}-n_s(n_2)^{2N}}{n_0(n_1)^{2N}+n_s(n_2)^{2N}}]^2,\n",
    "$$\n",
    "\n",
    "where $n_0=1$ is the refractive index of the superstrate, $n_s=n_{SiO_2}$ is the refractive index of the substrate. We will use this analytical solution to validate the accuracy of our simulations."
   ]
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i, N in enumerate(Ns):\n",
    "    sim_data = batch_results[f\"N={N:.2f}\"]\n",
    "    R = sim_data[\"R\"].flux  # extract reflectance data from the flux monitor\n",
    "    plt.plot(freqs / freq0, R, label=f\"N={N}\")  # plot reflectance spectrum\n",
    "\n",
    "    # plot the analytically calculated reflectance at the central frequency with a star marker\n",
    "    plt.scatter(\n",
    "        1,\n",
    "        (\n",
    "            ((n_tio2) ** (2 * N) - (n_sio2) ** (2 * N + 1))\n",
    "            / ((n_tio2) ** (2 * N) + (n_sio2) ** (2 * N + 1))\n",
    "        )\n",
    "        ** 2,\n",
    "        marker=\"*\",\n",
    "        s=50,\n",
    "    )\n",
    "\n",
    "plt.title(\"Reflectance of DBR of different number of periods\")\n",
    "plt.xlabel(\"Normalized frequency\")\n",
    "plt.ylabel(\"Reflectance\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0bd979cb",
   "metadata": {},
   "source": [
    "As the number of pairs increases, the reflectance at the stopband increases to unity. The width of the stopband agrees with the analytical expression discussed above. The analytical solution of the reflectance at the central frequency (stars) also agrees well with Tidy3D simulation results."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5ad1b17",
   "metadata": {},
   "source": [
    "## Modeling a High-Q DBR Microcavity"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4702ac74",
   "metadata": {},
   "source": [
    "Similar to a defect in a photonic crystal, an optical microcavity is formed when one layer of a DBR has an anomalous thickness. For example, if a high index layer in the middle of a DBR has a thickness of half a material wavelength instead of the usual quarter material wavelength, the DBR will show greatly suppressed reflection at the central frequency. The quality factor of this cavity mode can be very high if the total number of layers is large. DBR-based microcavities are widely used in lasers, filters, and sensing applications. \n",
    "\n",
    "To obtain the reflectance spectrum of a high-Q DBR cavity, a long simulation run time is required since we need to run the time stepping until the energy in the simulation domain dissipates. Fortunately, for 1D or 2D simulations, this is still an easy task. \n",
    "\n",
    "On the other hand, for a large 3D cavity with a very high Q value, this kind of simulation can become computationally impractical. In this case, the [ResonanceFinder](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.plugins.resonance.ResonanceFinder.html) plugin of Tidy3D is a handy tool. It can be used to extract the resonance frequencies and Q values without running the time stepping until the resonance modes fully decay, as demonstrated in the [photonic crystal cavity example](https://www.flexcompute.com/tidy3d/examples/notebooks/OptimizedL3/)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33303304",
   "metadata": {},
   "source": [
    "### Simulation Setup "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "930a6064",
   "metadata": {},
   "source": [
    "To resolve the cavity resonance in the spectrum, we narrow the frequency range a bit compared to the previous simulations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c0eb2030",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-18T19:51:30.617526Z",
     "iopub.status.busy": "2023-08-18T19:51:30.617379Z",
     "iopub.status.idle": "2023-08-18T19:51:30.638611Z",
     "shell.execute_reply": "2023-08-18T19:51:30.637914Z"
    }
   },
   "outputs": [],
   "source": [
    "lda0 = 0.63  # central wavelength\n",
    "freq0 = td.C_0 / lda0  # central frequency\n",
    "freqs = freq0 * np.linspace(\n",
    "    0.9, 1.1, 1001\n",
    ")  # frequency range of interest. The range is narrowed compared to previous simulations"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1dfb9aab",
   "metadata": {},
   "source": [
    "To create a DBR cavity, we will use the previously defined `build_layers` function twice to construct the regular DBRs on the top and bottom of the cavity. Then a single layer of $TiO_2$ with thickness $\\lambda_0/2n_{TiO_2}$ is added between them. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e3c9ded3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-18T19:51:30.641317Z",
     "iopub.status.busy": "2023-08-18T19:51:30.641162Z",
     "iopub.status.idle": "2023-08-18T19:51:31.034250Z",
     "shell.execute_reply": "2023-08-18T19:51:31.033615Z"
    },
    "tags": []
   },
   "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\">           </span><span style=\"color: #800000; text-decoration-color: #800000\">WARNING: Default value for the field monitor          </span> <a href=\"file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/components/monitor.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">monitor.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/components/monitor.py#261\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">261</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span><span style=\"color: #008000; text-decoration-color: #008000\">'colocate'</span><span style=\"color: #800000; text-decoration-color: #800000\"> setting has 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: #7f7f7f; text-decoration-color: #7f7f7f\">              </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </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 colocated to the  </span> <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">              </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span><span style=\"color: #800000; text-decoration-color: #800000\">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 fields </span> <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">              </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span><span style=\"color: #800000; text-decoration-color: #800000\">on the Yee grid instead.                              </span> <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">              </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]8;id=225485;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/components/monitor.py\u001b\\\u001b[2mmonitor.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=480022;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/components/monitor.py#261\u001b\\\u001b[2m261\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m           \u001b[0m\u001b[32m'colocate'\u001b[0m\u001b[31m setting has changed to \u001b[0m\u001b[32m'True'\u001b[0m\u001b[31m in Tidy3D    \u001b[0m \u001b[2m              \u001b[0m\n",
       "\u001b[2;36m           \u001b[0m\u001b[1;36m2.4\u001b[0m\u001b[31m.\u001b[0m\u001b[1;36m0\u001b[0m\u001b[31m. All field components will be colocated to the  \u001b[0m \u001b[2m              \u001b[0m\n",
       "\u001b[2;36m           \u001b[0m\u001b[31mgrid boundaries. Set to \u001b[0m\u001b[32m'False'\u001b[0m\u001b[31m to get the raw fields \u001b[0m \u001b[2m              \u001b[0m\n",
       "\u001b[2;36m           \u001b[0m\u001b[31mon the Yee grid instead.                              \u001b[0m \u001b[2m              \u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "N_bottom = 6  # number of layer pairs for the bottom DBR\n",
    "thickness_bottom = N_bottom * (\n",
    "    lda0 / (4 * n_tio2) + lda0 / (4 * n_sio2)\n",
    ")  # thickness of the bottom DBR\n",
    "bottom_DBR = build_layers(n_tio2, n_sio2, N_bottom, 0)  # construct the bottom DBR\n",
    "\n",
    "thickness_cavity = lda0 / (2 * n_tio2)  # thickness of the cavity layer\n",
    "# construct the cavity layer\n",
    "cavity = td.Structure(\n",
    "    geometry=td.Box.from_bounds(\n",
    "        rmin=(-inf_eff, -inf_eff, thickness_bottom),\n",
    "        rmax=(inf_eff, inf_eff, thickness_bottom + thickness_cavity),\n",
    "    ),\n",
    "    medium=td.Medium(permittivity=n_tio2**2),\n",
    ")\n",
    "\n",
    "N_top = 6  # number of layer pairs for the top DBR\n",
    "# construct the bottom DBR\n",
    "top_DBR = build_layers(n_sio2, n_tio2, N_top, thickness_bottom + thickness_cavity)\n",
    "thickness_top = N_top = N_bottom * (\n",
    "    lda0 / (4 * n_tio2) + lda0 / (4 * n_sio2)\n",
    ")  # thickness of the bottom DBR\n",
    "\n",
    "# construct the substrate\n",
    "sub = td.Structure(\n",
    "    geometry=td.Box.from_bounds(rmin=(-inf_eff, -inf_eff, -inf_eff), rmax=(inf_eff, inf_eff, 0)),\n",
    "    medium=td.Medium(permittivity=n_s**2),\n",
    ")\n",
    "\n",
    "# combining the top DBR, the bottom DBR, the cavity layer, and the substrate\n",
    "DBR_cavity = bottom_DBR + top_DBR\n",
    "DBR_cavity.append(cavity)\n",
    "DBR_cavity.append(sub)\n",
    "\n",
    "thickness = thickness_bottom + thickness_cavity + thickness_top  # total thickness of the device\n",
    "\n",
    "fwidth = 0.1 * freq0  # width of the frequency range\n",
    "\n",
    "# add a plane wave source\n",
    "plane_wave = td.PlaneWave(\n",
    "    source_time=td.GaussianPulse(freq0=freq0, fwidth=fwidth),\n",
    "    size=(td.inf, td.inf, 0),\n",
    "    center=(0, 0, thickness + lda0 / 4),\n",
    "    direction=\"-\",\n",
    "    pol_angle=0,\n",
    ")\n",
    "\n",
    "# add a flux monitor to measure reflectance\n",
    "flux_monitor = td.FluxMonitor(\n",
    "    center=(0, 0, thickness + lda0 / 2), size=(td.inf, td.inf, 0), freqs=freqs, name=\"R\"\n",
    ")\n",
    "\n",
    "# add a field monitor to measure the field distribution\n",
    "field_monitor = td.FieldMonitor(\n",
    "    center=(0, 0, 0), size=(td.inf, td.inf, td.inf), freqs=freqs, name=\"field\"\n",
    ")\n",
    "\n",
    "# simulation domain size in z direction\n",
    "Lz = thickness + 2 * lda0\n",
    "\n",
    "# run time needs to be sufficiently long to ensure the field decays away in the end\n",
    "run_time = 500 / fwidth\n",
    "\n",
    "sim = td.Simulation(\n",
    "    size=(0, 0, Lz),\n",
    "    center=(0, 0, thickness / 2),\n",
    "    grid_spec=td.GridSpec.auto(min_steps_per_wvl=60, wavelength=lda0),\n",
    "    structures=DBR_cavity,\n",
    "    sources=[plane_wave],\n",
    "    monitors=[flux_monitor, field_monitor],\n",
    "    run_time=run_time,\n",
    "    boundary_spec=td.BoundarySpec(\n",
    "        x=td.Boundary.periodic(), y=td.Boundary.periodic(), z=td.Boundary.pml()\n",
    "    ),\n",
    "    shutoff=1e-7,\n",
    ")  # early shutoff level is decreased to 1e-7 to increase the simulation accuracy\n",
    "\n",
    "# visualize the simulation setup\n",
    "ax = sim.plot(y=0)\n",
    "ax.get_xaxis().set_visible(False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9eb096c3",
   "metadata": {},
   "source": [
    "Submit the simulation job to the server."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "20b4d1f3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-18T19:51:31.036801Z",
     "iopub.status.busy": "2023-08-18T19:51:31.036614Z",
     "iopub.status.idle": "2023-08-18T19:53:02.349847Z",
     "shell.execute_reply": "2023-08-18T19:53:02.349116Z"
    }
   },
   "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\">[12:51:31] </span>Created task <span style=\"color: #008000; text-decoration-color: #008000\">'dbr_cavity'</span> with task_id                  <a href=\"file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">webapi.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#188\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">188</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span><span style=\"color: #008000; text-decoration-color: #008000\">'fdve-1c505fa8-bd32-4cf5-aec7-24d7ad11df36v1'</span>.          <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">             </span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m[12:51:31]\u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'dbr_cavity'\u001b[0m with task_id                  \u001b]8;id=24336;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=966816;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#188\u001b\\\u001b[2m188\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m           \u001b[0m\u001b[32m'fdve-1c505fa8-bd32-4cf5-aec7-24d7ad11df36v1'\u001b[0m.          \u001b[2m             \u001b[0m\n"
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    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>View task using web UI at                               <a href=\"file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">webapi.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#190\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">190</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-1c505fa8-bd32-4cf5-aec7-24d7ad11df36v1\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'https://tidy3d.simulation.cloud/workbench?taskId=fdve-</span></a> <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">             </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-1c505fa8-bd32-4cf5-aec7-24d7ad11df36v1\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">1c505fa8-bd32-4cf5-aec7-24d7ad11df36v1'</span></a>.                <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">             </span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0mView task using web UI at                               \u001b]8;id=517302;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=301350;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#190\u001b\\\u001b[2m190\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m           \u001b[0m\u001b]8;id=527713;https://tidy3d.simulation.cloud/workbench?taskId=fdve-1c505fa8-bd32-4cf5-aec7-24d7ad11df36v1\u001b\\\u001b[32m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=98253;https://tidy3d.simulation.cloud/workbench?taskId=fdve-1c505fa8-bd32-4cf5-aec7-24d7ad11df36v1\u001b\\\u001b[32mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=527713;https://tidy3d.simulation.cloud/workbench?taskId=fdve-1c505fa8-bd32-4cf5-aec7-24d7ad11df36v1\u001b\\\u001b[32m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=550961;https://tidy3d.simulation.cloud/workbench?taskId=fdve-1c505fa8-bd32-4cf5-aec7-24d7ad11df36v1\u001b\\\u001b[32mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=527713;https://tidy3d.simulation.cloud/workbench?taskId=fdve-1c505fa8-bd32-4cf5-aec7-24d7ad11df36v1\u001b\\\u001b[32m-\u001b[0m\u001b]8;;\u001b\\ \u001b[2m             \u001b[0m\n",
       "\u001b[2;36m           \u001b[0m\u001b]8;id=527713;https://tidy3d.simulation.cloud/workbench?taskId=fdve-1c505fa8-bd32-4cf5-aec7-24d7ad11df36v1\u001b\\\u001b[32m1c505fa8-bd32-4cf5-aec7-24d7ad11df36v1'\u001b[0m\u001b]8;;\u001b\\.                \u001b[2m             \u001b[0m\n"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "813e4900cc5d411e814689a78d905317",
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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": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>status = queued                                         <a href=\"file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">webapi.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#361\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">361</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0mstatus = queued                                         \u001b]8;id=59423;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=658754;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#361\u001b\\\u001b[2m361\u001b[0m\u001b]8;;\u001b\\\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[12:51:40] </span>status = preprocess                                     <a href=\"file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">webapi.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#355\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">355</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m[12:51:40]\u001b[0m\u001b[2;36m \u001b[0mstatus = preprocess                                     \u001b]8;id=302871;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=621602;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#355\u001b\\\u001b[2m355\u001b[0m\u001b]8;;\u001b\\\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[12:51:43] </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.025</span>. Use                     <a href=\"file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">webapi.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#341\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">341</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span><span style=\"color: #008000; text-decoration-color: #008000\">'web.real_cost(task_id)'</span> to get the billed FlexCredit   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">             </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>cost after a simulation run.                            <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">             </span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m[12:51:43]\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m0.025\u001b[0m. Use                     \u001b]8;id=920776;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=245579;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#341\u001b\\\u001b[2m341\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m           \u001b[0m\u001b[32m'web.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m to get the billed FlexCredit   \u001b[2m             \u001b[0m\n",
       "\u001b[2;36m           \u001b[0mcost after a simulation run.                            \u001b[2m             \u001b[0m\n"
      ]
     },
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    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>starting up solver                                      <a href=\"file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">webapi.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#377\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">377</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0mstarting up solver                                      \u001b]8;id=424087;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=305913;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#377\u001b\\\u001b[2m377\u001b[0m\u001b]8;;\u001b\\\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>running solver                                          <a href=\"file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">webapi.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#386\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">386</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0mrunning solver                                          \u001b]8;id=998244;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=242727;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#386\u001b\\\u001b[2m386\u001b[0m\u001b]8;;\u001b\\\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>To cancel the simulation, use <span style=\"color: #008000; text-decoration-color: #008000\">'web.abort(task_id)'</span> or   <a href=\"file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">webapi.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#387\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">387</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span><span style=\"color: #008000; text-decoration-color: #008000\">'web.delete(task_id)'</span> or abort/delete the task in the   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">             </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>web UI. Terminating the Python script will not stop the <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">             </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>job running on the cloud.                               <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">             </span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0mTo cancel the simulation, use \u001b[32m'web.abort\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m or   \u001b]8;id=996931;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=688199;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#387\u001b\\\u001b[2m387\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m           \u001b[0m\u001b[32m'web.delete\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m or abort/delete the task in the   \u001b[2m             \u001b[0m\n",
       "\u001b[2;36m           \u001b[0mweb UI. Terminating the Python script will not stop the \u001b[2m             \u001b[0m\n",
       "\u001b[2;36m           \u001b[0mjob running on the cloud.                               \u001b[2m             \u001b[0m\n"
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     },
     "metadata": {},
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       "Output()"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[12:52:48] </span>early shutoff detected, exiting.                        <a href=\"file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">webapi.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#404\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">404</span></a>\n",
       "</pre>\n"
      ],
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       "\u001b[2;36m[12:52:48]\u001b[0m\u001b[2;36m \u001b[0mearly shutoff detected, exiting.                        \u001b]8;id=379906;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=156154;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#404\u001b\\\u001b[2m404\u001b[0m\u001b]8;;\u001b\\\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\"></pre>\n"
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     },
     "metadata": {},
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    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
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       "\n"
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    {
     "data": {
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>status = postprocess                                    <a href=\"file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">webapi.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#419\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">419</span></a>\n",
       "</pre>\n"
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      "text/plain": [
       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0mstatus = postprocess                                    \u001b]8;id=375909;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=850440;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#419\u001b\\\u001b[2m419\u001b[0m\u001b]8;;\u001b\\\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\">[12:52:59] </span>status = success                                        <a href=\"file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">webapi.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#426\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">426</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m[12:52:59]\u001b[0m\u001b[2;36m \u001b[0mstatus = success                                        \u001b]8;id=921804;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=958018;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#426\u001b\\\u001b[2m426\u001b[0m\u001b]8;;\u001b\\\n"
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       "Output()"
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
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     },
     "metadata": {},
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    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[12:53:02] </span>loading SimulationData from data/simulation.hdf5        <a href=\"file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">webapi.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#590\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">590</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m[12:53:02]\u001b[0m\u001b[2;36m \u001b[0mloading SimulationData from data/simulation.hdf5        \u001b]8;id=201070;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=309220;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#590\u001b\\\u001b[2m590\u001b[0m\u001b]8;;\u001b\\\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sim_data = web.run(sim, task_name=\"dbr_cavity\", path=\"data/simulation.hdf5\", verbose=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "293b98dc",
   "metadata": {},
   "source": [
    "### Result Visualization "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ff29699",
   "metadata": {},
   "source": [
    "Once the simulation is complete, we can plot the reflectance spectra. A sharp dip at the central frequency due to the cavity resonance mode is observed, in agreement with our expectation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "7e966b70",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-18T19:53:04.464151Z",
     "iopub.status.busy": "2023-08-18T19:53:04.464018Z",
     "iopub.status.idle": "2023-08-18T19:53:04.625619Z",
     "shell.execute_reply": "2023-08-18T19:53:04.625045Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "R = sim_data[\"R\"].flux  # extracting reflection data from the flux monitor\n",
    "plt.plot(freqs / freq0, R)\n",
    "plt.xlabel(\"Normalized frequency\")\n",
    "plt.ylabel(\"Reflectance\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5da1c2f8",
   "metadata": {},
   "source": [
    "Finally, we visualize the frequency domain field distribution. Since the simulation is in 1D, we can not plot the field as a 2D false color image. Instead, we can plot it as a curve. The incident plane wave is polarized in the x direction, so we will look at the x component of the field. \n",
    "\n",
    "At the resonance frequency, the field distribution shows a strong localization at the cavity. On the contrary, when the frequency is slightly off-resonance, the field is exponentially decaying, leading to strong reflection. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "5304a252",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-18T19:53:04.627924Z",
     "iopub.status.busy": "2023-08-18T19:53:04.627727Z",
     "iopub.status.idle": "2023-08-18T19:53:05.071641Z",
     "shell.execute_reply": "2023-08-18T19:53:05.071054Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, (ax1, ax2) = plt.subplots(1, 2, tight_layout=True, figsize=(8, 4))\n",
    "\n",
    "# plot the field distribution at the resonance frequency\n",
    "np.squeeze(sim_data[\"field\"].Ex.sel(f=freq0)).abs.plot(ax=ax1)\n",
    "ax1.set_title(\"|Ex(x, y)| at $f_0$\")\n",
    "\n",
    "# plot the field distribution at the off-resonance frequency\n",
    "np.squeeze(sim_data[\"field\"].Ex.sel(f=0.9 * freq0)).abs.plot(ax=ax2)\n",
    "ax2.set_title(\"|Ex(x, y)| at $0.9f_0$\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bfb8b98d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "applications": [
   "Metamaterials, gratings, and other periodic structures"
  ],
  "description": "This notebook demonstrates how to model distributed Bragg reflector and cavity in Tidy3D FDTD.",
  "feature_image": "./img/dbr_schematic.png",
  "features": [
   "Parameter sweep",
   "2D simulation"
  ],
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "keywords": "distributed bragg reflector, DBR, cavity, 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"
  },
  "title": "Distributed Bragg Reflector and Cavity | Flexcompute",
  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "state": {
     "09a6392e66064180940d99f350a67ac9": {
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