{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a65a6737",
   "metadata": {},
   "source": [
    "# Distributed Bragg reflector and cavity"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47e433db",
   "metadata": {},
   "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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "67c41dde",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-18T20:37:15.832571Z",
     "iopub.status.busy": "2023-08-18T20:37:15.832397Z",
     "iopub.status.idle": "2023-08-18T20:37:17.364869Z",
     "shell.execute_reply": "2023-08-18T20:37:17.364281Z"
    }
   },
   "outputs": [],
   "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",
   "metadata": {},
   "source": [
    "## Simulating the Stopband of a DBR"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f150734b",
   "metadata": {},
   "source": [
    "### Simulation Setup "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86d2bbb5",
   "metadata": {},
   "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. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3c528785",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-18T20:37:17.367543Z",
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     "shell.execute_reply": "2023-08-18T20:37:17.386151Z"
    }
   },
   "outputs": [],
   "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a80a24bf",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-18T20:37:17.388764Z",
     "iopub.status.busy": "2023-08-18T20:37:17.388580Z",
     "iopub.status.idle": "2023-08-18T20:37:17.407707Z",
     "shell.execute_reply": "2023-08-18T20:37:17.407142Z"
    }
   },
   "outputs": [],
   "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"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c710663",
   "metadata": {},
   "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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2a4913bb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-18T20:37:17.410170Z",
     "iopub.status.busy": "2023-08-18T20:37:17.409972Z",
     "iopub.status.idle": "2023-08-18T20:37:17.430389Z",
     "shell.execute_reply": "2023-08-18T20:37:17.429751Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "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}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7be52b05",
   "metadata": {},
   "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. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8c34ee9c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-18T20:37:17.432914Z",
     "iopub.status.busy": "2023-08-18T20:37:17.432723Z",
     "iopub.status.idle": "2023-08-18T20:37:17.453605Z",
     "shell.execute_reply": "2023-08-18T20:37:17.453066Z"
    }
   },
   "outputs": [],
   "source": [
    "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)  # thickness 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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "426ca532",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-18T20:37:17.455953Z",
     "iopub.status.busy": "2023-08-18T20:37:17.455752Z",
     "iopub.status.idle": "2023-08-18T20:37:17.480536Z",
     "shell.execute_reply": "2023-08-18T20:37:17.479953Z"
    }
   },
   "outputs": [],
   "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",
   "execution_count": 7,
   "id": "c42cfd32",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-18T20:37:17.482905Z",
     "iopub.status.busy": "2023-08-18T20:37:17.482704Z",
     "iopub.status.idle": "2023-08-18T20:37:17.544948Z",
     "shell.execute_reply": "2023-08-18T20:37:17.544398Z"
    },
    "tags": []
   },
   "outputs": [],
   "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. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "20157b64",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-18T20:37:17.547356Z",
     "iopub.status.busy": "2023-08-18T20:37:17.547157Z",
     "iopub.status.idle": "2023-08-18T20:37:17.925664Z",
     "shell.execute_reply": "2023-08-18T20:37:17.924900Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "model_id": "e5350dedb35645419f8834fcef35f351",
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       "Output()"
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     "output_type": "display_data"
    },
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     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></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\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">20:00:46 CEST </span>Started working on Batch containing <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">5</span> tasks.                      \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m20:00:46 CEST\u001b[0m\u001b[2;36m \u001b[0mStarted working on Batch containing \u001b[1;36m5\u001b[0m tasks.                      \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\">20:00:50 CEST </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.125</span> for the whole batch.               \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m20:00:50 CEST\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m0.125\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   \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span>the 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   \n",
       "\u001b[2;36m              \u001b[0mthe Batch has completed.                                          \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">20:05:06 CEST </span>Batch complete.                                                   \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m20:05:06 CEST\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"
      ],
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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": []
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "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 "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5624996",
   "metadata": {},
   "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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "75083244",
   "metadata": {
    "execution": {
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     "shell.execute_reply": "2023-08-18T19:51:30.578895Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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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": {
      "image/png": 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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\">20:05:18 CEST </span>Created task <span style=\"color: #008000; text-decoration-color: #008000\">'dbr_cavity'</span> with task_id                            \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span><span style=\"color: #008000; text-decoration-color: #008000\">'fdve-1324d142-b516-44ce-a2ec-d5459482eb01'</span> and task_type <span style=\"color: #008000; text-decoration-color: #008000\">'FDTD'</span>. \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m20:05:18 CEST\u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'dbr_cavity'\u001b[0m with task_id                            \n",
       "\u001b[2;36m              \u001b[0m\u001b[32m'fdve-1324d142-b516-44ce-a2ec-d5459482eb01'\u001b[0m and task_type \u001b[32m'FDTD'\u001b[0m. \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span>View task using web UI at                                         \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-1324d142-b516-44ce-a2ec-d5459482eb01\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'https://tidy3d.simulation.cloud/workbench?taskId=fdve-1324d142-b5</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-1324d142-b516-44ce-a2ec-d5459482eb01\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">16-44ce-a2ec-d5459482eb01'</span></a>.                                       \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m             \u001b[0m\u001b[2;36m \u001b[0mView task using web UI at                                         \n",
       "\u001b[2;36m              \u001b[0m\u001b]8;id=949474;https://tidy3d.simulation.cloud/workbench?taskId=fdve-1324d142-b516-44ce-a2ec-d5459482eb01\u001b\\\u001b[32m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=727872;https://tidy3d.simulation.cloud/workbench?taskId=fdve-1324d142-b516-44ce-a2ec-d5459482eb01\u001b\\\u001b[32mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=949474;https://tidy3d.simulation.cloud/workbench?taskId=fdve-1324d142-b516-44ce-a2ec-d5459482eb01\u001b\\\u001b[32m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=399834;https://tidy3d.simulation.cloud/workbench?taskId=fdve-1324d142-b516-44ce-a2ec-d5459482eb01\u001b\\\u001b[32mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=949474;https://tidy3d.simulation.cloud/workbench?taskId=fdve-1324d142-b516-44ce-a2ec-d5459482eb01\u001b\\\u001b[32m-1324d142-b5\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m              \u001b[0m\u001b]8;id=949474;https://tidy3d.simulation.cloud/workbench?taskId=fdve-1324d142-b516-44ce-a2ec-d5459482eb01\u001b\\\u001b[32m16-44ce-a2ec-d5459482eb01'\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>Task folder: <a href=\"https://tidy3d.simulation.cloud/folders/folder-7a0ee478-ee62-43e0-9a9e-26a06b299b0a\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'default'</span></a>.                                           \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m             \u001b[0m\u001b[2;36m \u001b[0mTask folder: \u001b]8;id=707221;https://tidy3d.simulation.cloud/folders/folder-7a0ee478-ee62-43e0-9a9e-26a06b299b0a\u001b\\\u001b[32m'default'\u001b[0m\u001b]8;;\u001b\\.                                           \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "eb7043edf4d4469bbcc0cd8c25539349",
       "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\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">20:05:20 CEST </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.025</span>. Minimum cost depends on task      \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span>execution details. Use <span style=\"color: #008000; text-decoration-color: #008000\">'web.real_cost(task_id)'</span> to get the billed \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span>FlexCredit cost after a simulation run.                           \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m20:05:20 CEST\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m0.025\u001b[0m. Minimum cost depends on task      \n",
       "\u001b[2;36m              \u001b[0mexecution details. Use \u001b[32m'web.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m to get the billed \n",
       "\u001b[2;36m              \u001b[0mFlexCredit cost after a simulation run.                           \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">20:05:21 CEST </span>status = queued                                                   \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m20:05:21 CEST\u001b[0m\u001b[2;36m \u001b[0mstatus = queued                                                   \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span>To cancel the simulation, use <span style=\"color: #008000; text-decoration-color: #008000\">'web.abort(task_id)'</span> or             \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span><span style=\"color: #008000; text-decoration-color: #008000\">'web.delete(task_id)'</span> or abort/delete the task in the web UI.     \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span>Terminating the Python script will not stop the job running on the\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span>cloud.                                                            \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m             \u001b[0m\u001b[2;36m \u001b[0mTo cancel the simulation, use \u001b[32m'web.abort\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m or             \n",
       "\u001b[2;36m              \u001b[0m\u001b[32m'web.delete\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m or abort/delete the task in the web UI.     \n",
       "\u001b[2;36m              \u001b[0mTerminating the Python script will not stop the job running on the\n",
       "\u001b[2;36m              \u001b[0mcloud.                                                            \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1c13875c079249eab11785b27fc14ee0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">20:30:15 CEST </span>status = preprocess                                               \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m20:30:15 CEST\u001b[0m\u001b[2;36m \u001b[0mstatus = preprocess                                               \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">20:30:22 CEST </span>starting up solver                                                \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m20:30:22 CEST\u001b[0m\u001b[2;36m \u001b[0mstarting up solver                                                \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span>running solver                                                    \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m             \u001b[0m\u001b[2;36m \u001b[0mrunning solver                                                    \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "46614ebe80bc41188dbb42861f37b8ad",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">20:31:10 CEST </span>early shutoff detected at <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">64</span>%, exiting.                           \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m20:31:10 CEST\u001b[0m\u001b[2;36m \u001b[0mearly shutoff detected at \u001b[1;36m64\u001b[0m%, exiting.                           \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span>status = postprocess                                              \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m             \u001b[0m\u001b[2;36m \u001b[0mstatus = postprocess                                              \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "73fe6528f75c4fd598feda7d98851035",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">20:31:12 CEST </span>status = success                                                  \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m20:31:12 CEST\u001b[0m\u001b[2;36m \u001b[0mstatus = success                                                  \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">20:31:14 CEST </span>View simulation result at                                         \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-1324d142-b516-44ce-a2ec-d5459482eb01\" target=\"_blank\"><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">'https://tidy3d.simulation.cloud/workbench?taskId=fdve-1324d142-b5</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-1324d142-b516-44ce-a2ec-d5459482eb01\" target=\"_blank\"><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">16-44ce-a2ec-d5459482eb01'</span></a><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">.</span>                                       \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m20:31:14 CEST\u001b[0m\u001b[2;36m \u001b[0mView simulation result at                                         \n",
       "\u001b[2;36m              \u001b[0m\u001b]8;id=371415;https://tidy3d.simulation.cloud/workbench?taskId=fdve-1324d142-b516-44ce-a2ec-d5459482eb01\u001b\\\u001b[4;34m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=839834;https://tidy3d.simulation.cloud/workbench?taskId=fdve-1324d142-b516-44ce-a2ec-d5459482eb01\u001b\\\u001b[4;34mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=371415;https://tidy3d.simulation.cloud/workbench?taskId=fdve-1324d142-b516-44ce-a2ec-d5459482eb01\u001b\\\u001b[4;34m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=649923;https://tidy3d.simulation.cloud/workbench?taskId=fdve-1324d142-b516-44ce-a2ec-d5459482eb01\u001b\\\u001b[4;34mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=371415;https://tidy3d.simulation.cloud/workbench?taskId=fdve-1324d142-b516-44ce-a2ec-d5459482eb01\u001b\\\u001b[4;34m-1324d142-b5\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m              \u001b[0m\u001b]8;id=371415;https://tidy3d.simulation.cloud/workbench?taskId=fdve-1324d142-b516-44ce-a2ec-d5459482eb01\u001b\\\u001b[4;34m16-44ce-a2ec-d5459482eb01'\u001b[0m\u001b]8;;\u001b\\\u001b[4;34m.\u001b[0m                                       \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1c131a8a34e34f6a89edecb67284cf0f",
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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\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">20:31:17 CEST </span>loading simulation from data/simulation.hdf5                      \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m20:31:17 CEST\u001b[0m\u001b[2;36m \u001b[0mloading simulation from data/simulation.hdf5                      \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>"
      ]
     },
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    }
   ],
   "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()"
   ]
  }
 ],
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  "description": "This notebook demonstrates how to model distributed Bragg reflector and cavity in Tidy3D FDTD.",
  "feature_image": "./img/dbr_schematic.png",
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