{ "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", "" ] }, { "cell_type": "code", "execution_count": 1, "id": "67c41dde", "metadata": { "execution": { "iopub.execute_input": "2023-02-03T04:23:26.181445Z", "iopub.status.busy": "2023-02-03T04:23:26.181145Z", "iopub.status.idle": "2023-02-03T04:23:27.222307Z", "shell.execute_reply": "2023-02-03T04:23:27.221901Z" } }, "outputs": [ { "data": { "text/html": [ "
[22:23:27] WARNING  This version of Tidy3D was pip installed from the 'tidy3d-beta' repository on   __init__.py:103\n",
       "                    PyPI. Future releases will be uploaded to the 'tidy3d' repository. From now on,                \n",
       "                    please use 'pip install tidy3d' instead.                                                       \n",
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           INFO     Using client version: 1.9.0rc1                                                  __init__.py:121\n",
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\n" ], "text/plain": [ "\u001b[2;36m \u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO \u001b[0m Using client version: \u001b[1;36m1.9\u001b[0m.0rc1 \u001b]8;id=942352;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/__init__.py\u001b\\\u001b[2m__init__.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=519148;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/__init__.py#121\u001b\\\u001b[2m121\u001b[0m\u001b]8;;\u001b\\\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import tidy3d as td\n", "import tidy3d.web as web\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n" ] }, { "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": "2023-02-03T04:23:27.225045Z", "iopub.status.busy": "2023-02-03T04:23:27.224563Z", "iopub.status.idle": "2023-02-03T04:23:27.227290Z", "shell.execute_reply": "2023-02-03T04:23:27.226918Z" } }, "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\n" ] }, { "cell_type": "code", "execution_count": 3, "id": "a80a24bf", "metadata": { "execution": { "iopub.execute_input": "2023-02-03T04:23:27.229320Z", "iopub.status.busy": "2023-02-03T04:23:27.229161Z", "iopub.status.idle": "2023-02-03T04:23:27.231333Z", "shell.execute_reply": "2023-02-03T04:23:27.230980Z" } }, "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\n" ] }, { "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": "2023-02-03T04:23:27.233251Z", "iopub.status.busy": "2023-02-03T04:23:27.233103Z", "iopub.status.idle": "2023-02-03T04:23:27.235568Z", "shell.execute_reply": "2023-02-03T04:23:27.235219Z" } }, "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}\")\n" ] }, { "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": "2023-02-03T04:23:27.237635Z", "iopub.status.busy": "2023-02-03T04:23:27.237469Z", "iopub.status.idle": "2023-02-03T04:23:27.241925Z", "shell.execute_reply": "2023-02-03T04:23:27.241563Z" } }, "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) # 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\n" ] }, { "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": "2023-02-03T04:23:27.244115Z", "iopub.status.busy": "2023-02-03T04:23:27.243785Z", "iopub.status.idle": "2023-02-03T04:23:27.250172Z", "shell.execute_reply": "2023-02-03T04:23:27.249820Z" } }, "outputs": [], "source": [ "def make_DBR(N):\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 * (\n", " lda0 / (4 * n_tio2) + lda0 / (4 * n_sio2)\n", " ) # 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(x=td.Boundary.periodic(), y=td.Boundary.periodic(), z=td.Boundary.pml()), # 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\n" ] }, { "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": "2023-02-03T04:23:27.252237Z", "iopub.status.busy": "2023-02-03T04:23:27.252081Z", "iopub.status.idle": "2023-02-03T04:23:27.266886Z", "shell.execute_reply": "2023-02-03T04:23:27.266497Z" }, "tags": [] }, "outputs": [], "source": [ "Ns = np.array([2, 3, 4, 5, 10]) # collection of N for the parameter sweep\n", "sims = {\n", " f\"N={N:.2f}\": make_DBR(N) for N in Ns\n", "} # construct simulations for each N from Ns\n" ] }, { "cell_type": "markdown", "id": "6568d3d7", "metadata": {}, "source": [ "Submit the batch to the server. 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    "batch = web.Batch(simulations=sims)\n",
    "batch_results = batch.run(path_dir=\"data\")\n"
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  {
   "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."
   ]
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  {
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   "execution_count": 9,
   "id": "75083244",
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     "shell.execute_reply": "2023-02-03T04:24:09.744098Z"
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       "\u001b[2;36m[22:24:09]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m loading SimulationData from data/\u001b[93m616df681-e1c3-4b2e-b19d-d72eca7a6b5f\u001b[0m.hdf5        \u001b]8;id=510403;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=86242;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#415\u001b\\\u001b[2m415\u001b[0m\u001b]8;;\u001b\\\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       ""
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "
"
      ]
     },
     "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"
   ]
  },
  {
   "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/v1.9.0rc2/_autosummary/tidy3d.plugins.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://docs.flexcompute.com/projects/tidy3d/en/v1.9.0rc2/notebooks/OptimizedL3.html)."
   ]
  },
  {
   "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": "2023-02-03T04:24:09.782713Z",
     "iopub.status.busy": "2023-02-03T04:24:09.782598Z",
     "iopub.status.idle": "2023-02-03T04:24:09.784894Z",
     "shell.execute_reply": "2023-02-03T04:24:09.784534Z"
    }
   },
   "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\n"
   ]
  },
  {
   "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": "2023-02-03T04:24:09.787306Z",
     "iopub.status.busy": "2023-02-03T04:24:09.787062Z",
     "iopub.status.idle": "2023-02-03T04:24:09.944119Z",
     "shell.execute_reply": "2023-02-03T04:24:09.943754Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "
"
      ]
     },
     "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(\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",
    "# 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 = (\n",
    "    thickness_bottom + thickness_cavity + thickness_top\n",
    ")  # 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(x=td.Boundary.periodic(), y=td.Boundary.periodic(), z=td.Boundary.pml()),\n",
    "    shutoff=1e-7,\n",
    ")  # early shutoff level is decreased to 1e-7 to increase the simulation accuracy\n",
    "\n",
    "# visulize the simulation setup\n",
    "ax = sim.plot(y=0)\n",
    "ax.get_xaxis().set_visible(False)\n"
   ]
  },
  {
   "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": "2023-02-03T04:24:09.946311Z",
     "iopub.status.busy": "2023-02-03T04:24:09.946138Z",
     "iopub.status.idle": "2023-02-03T04:25:52.309778Z",
     "shell.execute_reply": "2023-02-03T04:25:52.309308Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "
[22:24:10] INFO     Created task 'dbr_cavity' with task_id 'e85c6954-d5f3-4094-9b7d-53de50021342'.    webapi.py:120\n",
       "
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      ],
      "text/plain": [
       "\u001b[2;36m[22:24:10]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m Created task \u001b[32m'dbr_cavity'\u001b[0m with task_id \u001b[32m'e85c6954-d5f3-4094-9b7d-53de50021342'\u001b[0m.    \u001b]8;id=498350;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=690159;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#120\u001b\\\u001b[2m120\u001b[0m\u001b]8;;\u001b\\\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
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      ],
      "text/plain": [
       "\u001b[?25l"
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     "metadata": {},
     "output_type": "display_data"
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    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
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[22:24:19] INFO     starting up solver                                                                webapi.py:278\n",
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[22:25:52] INFO     loading SimulationData from data/simulation.hdf5                                  webapi.py:415\n",
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       "\u001b[2;36m[22:25:52]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m loading SimulationData from data/simulation.hdf5                                  \u001b]8;id=507303;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=309284;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#415\u001b\\\u001b[2m415\u001b[0m\u001b]8;;\u001b\\\n"
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  {
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   "id": "293b98dc",
   "metadata": {},
   "source": [
    "### Result Visualization "
   ]
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  {
   "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": "2023-02-03T04:25:53.680385Z",
     "iopub.status.busy": "2023-02-03T04:25:53.680273Z",
     "iopub.status.idle": "2023-02-03T04:25:53.836350Z",
     "shell.execute_reply": "2023-02-03T04:25:53.835912Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Reflectance')"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "
"
      ]
     },
     "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"
   ]
  },
  {
   "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": "2023-02-03T04:25:53.839208Z",
     "iopub.status.busy": "2023-02-03T04:25:53.838989Z",
     "iopub.status.idle": "2023-02-03T04:25:54.230203Z",
     "shell.execute_reply": "2023-02-03T04:25:54.229783Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, '|Ex(x, y)| at $0.9f_0$')"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "
"
      ]
     },
     "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bfb8b98d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.9"
  },
  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "state": {
     "00f324e857c0475fa66c05c1a37a313c": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "2.0.0",
      "model_name": "LayoutModel",
      "state": {
       "_model_module": "@jupyter-widgets/base",
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N=2.00: status = success      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% 0:00:00\nN=3.00: status = success      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% 0:00:00\nN=4.00: status = success      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% 0:00:00\nN=5.00: status = success      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% 0:00:00\nN=10.00: status = postprocess ━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━━━━━━━━  71% 0:00:10\nN=2.00: status = success      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% 0:00:00\nN=3.00: status = success      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% 0:00:00\nN=4.00: status = success      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% 0:00:00\nN=5.00: status = success      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% 0:00:00\nN=10.00: status = postprocess ━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━━━━━━━━  71% 0:00:10
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          "text/plain": "\r\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2KN=2.00: status = success      \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m100%\u001b[0m \u001b[36m0:00:00\u001b[0m\nN=3.00: status = success      \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m100%\u001b[0m \u001b[36m0:00:00\u001b[0m\nN=4.00: status = success      \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m100%\u001b[0m \u001b[36m0:00:00\u001b[0m\nN=5.00: status = success      \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m100%\u001b[0m \u001b[36m0:00:00\u001b[0m\nN=10.00: status = postprocess \u001b[38;2;249;38;114m━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[38;2;249;38;114m╸\u001b[0m\u001b[38;5;237m━━━━━━━━━━━\u001b[0m \u001b[35m 71%\u001b[0m \u001b[36m0:00:10\u001b[0m\nN=2.00: status = success      \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m100%\u001b[0m \u001b[36m0:00:00\u001b[0m\nN=3.00: status = success      \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m100%\u001b[0m \u001b[36m0:00:00\u001b[0m\nN=4.00: status = success      \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m100%\u001b[0m \u001b[36m0:00:00\u001b[0m\nN=5.00: status = success      \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m100%\u001b[0m \u001b[36m0:00:00\u001b[0m\nN=10.00: status = postprocess \u001b[38;2;249;38;114m━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[38;2;249;38;114m╸\u001b[0m\u001b[38;5;237m━━━━━━━━━━━\u001b[0m \u001b[35m 71%\u001b[0m \u001b[36m0:00:10\u001b[0m"
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