{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Narrowband mid-IR thermal emitters via quasi-BIC metasurfaces\n",
    "\n",
    "**Author: Guodong Zhu, Vanderbilt University**\n",
    "\n",
    "In this instance, we replicate the absorption simulation as detailed by the work `Sen Yang, Mingze He, Chuchuan Hong, Josh Nordlander, Jon-Paul Maria, Joshua D. Caldwell, and Justus C. Ndukaife, \"Single-peak and narrow-band mid-infrared thermal emitters driven by mirror-coupled plasmonic quasi-BIC metasurfaces,\" Optica 11, 305-314 (2024)` [DOI:10.1364/OPTICA.514203](https://doi.org/10.1364/OPTICA.514203). In this notebook, we emulate the same structure and demonstrate that our resultant absorption spectrum aligns closely with their published results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pylab as plt\n",
    "import numpy as np\n",
    "import tidy3d as td\n",
    "import tidy3d.web as web\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Simulation Setup"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define global variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "um = 1e0\n",
    "nm = 1e-3\n",
    "wavelength = 4 # central wavelength\n",
    "freq = td.C_0 / wavelength # central frequency\n",
    "\n",
    "# define frequency range\n",
    "freq_high = td.C_0 / (wavelength - 1 * um)\n",
    "freq_low = td.C_0 / (wavelength + 1 * um)\n",
    "fwidth = freq_high - freq_low\n",
    "spectrum_freq = np.linspace(freq - fwidth / 2, freq + fwidth / 2, 1000)\n",
    "run_time = 200 / fwidth\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define materials."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "medium_spacer = td.Medium(permittivity=1.685**2)\n",
    "medium_gold = td.material_library[\"Au\"][\"Olmon2012evaporated\"]\n",
    "medium_substrate = td.Medium(permittivity=1.64**2)\n",
    "eps_background = 1.0\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define geometric parameters used in the simulation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "thick_gold = 200 * nm\n",
    "thick_sub = 4.2\n",
    "thick_spacer = 0.65\n",
    "thick_reflector = 0.15\n",
    "thick_air = 4.8\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define the simulation domain size."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "size_x = 2100 * nm\n",
    "size_y = 2100 * nm\n",
    "size_z = thick_sub + thick_gold + thick_air + thick_spacer + thick_reflector  # totsl 10 um\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate the center positions of the structures."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "top_z = size_z / 2.0\n",
    "center_air_z = top_z - thick_air / 2.0\n",
    "center_metal_z = top_z - thick_air - thick_gold / 2.0\n",
    "center_spacer_z = top_z - thick_air - thick_gold - thick_spacer / 2.0\n",
    "center_reflector_z = top_z - thick_air - thick_gold - thick_spacer - thick_reflector / 2.0\n",
    "center_sub_z = top_z - thick_air - thick_gold - thick_spacer - thick_reflector - thick_sub / 2.0\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create the structures."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tidy3d import BoundarySpec, Boundary\n",
    "\n",
    "reflector = td.Structure(\n",
    "    geometry=td.Box.from_bounds(\n",
    "        rmin=(-td.inf, -td.inf, center_reflector_z - thick_reflector / 2.0),\n",
    "        rmax=(+td.inf, +td.inf, center_reflector_z + thick_reflector / 2.0),\n",
    "    ),\n",
    "    medium=medium_gold,\n",
    "    name=\"reflector\",\n",
    ")\n",
    "\n",
    "spacer = td.Structure(\n",
    "    geometry=td.Box.from_bounds(\n",
    "        rmin=(-td.inf, -td.inf, center_spacer_z - thick_spacer / 2.0),\n",
    "        rmax=(+td.inf, +td.inf, center_spacer_z + thick_spacer / 2.0),\n",
    "    ),\n",
    "    medium=medium_spacer,\n",
    "    name=\"spacer\",\n",
    ")\n",
    "\n",
    "substrate = td.Structure(\n",
    "    geometry=td.Box(\n",
    "        center=(0, 0, -size_z + thick_sub),\n",
    "        size=(td.inf, td.inf, size_z),\n",
    "    ),\n",
    "    medium=medium_substrate,\n",
    "    name=\"substrate\",\n",
    ")\n",
    "\n",
    "# (1) make a cylinder at origin\n",
    "# (2) scale 0.21 in x, 0.7 in y\n",
    "# (3) rotate 6 degree w.r.t z\n",
    "# (4) move 0.525 um in +x direction\n",
    "ellipse_R = td.Structure(\n",
    "    geometry=td.Transformed(\n",
    "        geometry=td.Cylinder(radius=1, center=[0, 0, 0.1], length=0.2),\n",
    "        transform=[\n",
    "            [0.20884959802733738, -0.07316992428735743, 0, 0.525],\n",
    "            [0.021950977286207228, 0.6961653267577913, 0, 0],\n",
    "            [0, 0, 1, 0],\n",
    "            [0, 0, 0, 1],\n",
    "        ],\n",
    "    ),\n",
    "    medium=medium_gold,\n",
    "    name=\"ellipse_R\",\n",
    ")\n",
    "# similar to right, move in -x direction and rotate -6 degree\n",
    "ellipse_L = td.Structure(\n",
    "    geometry=td.Transformed(\n",
    "        geometry=td.Cylinder(radius=1, center=[0, 0, 0.1], length=0.2),\n",
    "        transform=[\n",
    "            [0.20884959802733738, 0.07316992428735743, 0, -0.525],\n",
    "            [-0.021950977286207228, 0.6961653267577913, 0, 0],\n",
    "            [0, 0, 1, 0],\n",
    "            [0, 0, 0, 1],\n",
    "        ],\n",
    "    ),\n",
    "    medium=medium_gold,\n",
    "    name=\"ellipse_L\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a plane wave source as excitation. Create field monitors and flux monitors to record the field distribution and far-field reflection."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "plane_wave = td.PlaneWave(\n",
    "    center=(0, 0, center_air_z),\n",
    "    size=(td.inf, td.inf, 0),\n",
    "    source_time=td.GaussianPulse(freq0=freq, fwidth=fwidth),\n",
    "    pol_angle=0,\n",
    "    angle_theta=0,\n",
    "    direction=\"-\",\n",
    ")\n",
    "\n",
    "field_monitor_xy = td.FieldMonitor(\n",
    "    center=(0, 0, center_metal_z),\n",
    "    size=(td.inf, td.inf, 0),\n",
    "    freqs=[freq],\n",
    "    name=\"field_xy\",\n",
    ")\n",
    "\n",
    "field_monitor_xz = td.FieldMonitor(\n",
    "    center=(0, 0, 0),\n",
    "    size=(td.inf, 0, td.inf),\n",
    "    freqs=[freq],\n",
    "    name=\"field_xz\",\n",
    ")\n",
    "\n",
    "reflection = td.FluxMonitor(\n",
    "    center=(0, 0, center_air_z + 1),\n",
    "    size=(td.inf, td.inf, 0),\n",
    "    freqs=spectrum_freq,\n",
    "    name=\"reflection\",\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Add a mesh override structure to locally refine the grid around the structure for better accuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "mesh_overrides = [\n",
    "    td.MeshOverrideStructure(\n",
    "        geometry=td.Box.from_bounds(\n",
    "            rmin=(-td.inf, -td.inf, center_reflector_z - thick_spacer / 2.0),\n",
    "            rmax=(+td.inf, +td.inf, center_metal_z + thick_gold / 2.0),\n",
    "        ),\n",
    "        dl=[40 * nm, 40 * nm, 20 * nm],\n",
    "    )\n",
    "]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a simulation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "ellipse_TE = td.Simulation(\n",
    "    size=(size_x, size_y, size_z),\n",
    "    run_time=run_time,\n",
    "    structures=[substrate, spacer, reflector, ellipse_R, ellipse_L],\n",
    "    sources=[plane_wave],\n",
    "    monitors=[field_monitor_xy, field_monitor_xz, reflection],\n",
    "    # grid_spec=td.GridSpec.uniform(dl=dl),\n",
    "    boundary_spec=td.BoundarySpec(\n",
    "        x=Boundary.periodic(),\n",
    "        y=Boundary.periodic(),\n",
    "        z=td.Boundary(plus=td.PML(num_layers=64), minus=td.PML(num_layers=12)),\n",
    "    ),\n",
    "    grid_spec=td.GridSpec.auto(\n",
    "        wavelength=wavelength,\n",
    "        min_steps_per_wvl=20,\n",
    "        override_structures=mesh_overrides,\n",
    "    ),\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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74948114500000.0, &quot;fwidth&quot;: 39972327733333.33, &quot;offset&quot;: 5.0, &quot;remove_dc_component&quot;: true}, &quot;direction&quot;: &quot;-&quot;, &quot;angle_theta&quot;: 0.0, &quot;angle_phi&quot;: 0.0, &quot;pol_angle&quot;: 0.0}], &quot;boundary_spec&quot;: {&quot;attrs&quot;: {}, &quot;x&quot;: {&quot;attrs&quot;: {}, &quot;plus&quot;: {&quot;attrs&quot;: {}, &quot;name&quot;: null, &quot;type&quot;: &quot;Periodic&quot;}, &quot;minus&quot;: {&quot;attrs&quot;: {}, &quot;name&quot;: null, &quot;type&quot;: &quot;Periodic&quot;}, &quot;type&quot;: &quot;Boundary&quot;}, &quot;y&quot;: {&quot;attrs&quot;: {}, &quot;plus&quot;: {&quot;attrs&quot;: {}, &quot;name&quot;: null, &quot;type&quot;: &quot;Periodic&quot;}, &quot;minus&quot;: {&quot;attrs&quot;: {}, &quot;name&quot;: null, &quot;type&quot;: &quot;Periodic&quot;}, &quot;type&quot;: &quot;Boundary&quot;}, &quot;z&quot;: {&quot;attrs&quot;: {}, &quot;plus&quot;: {&quot;attrs&quot;: {}, &quot;name&quot;: null, 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       "    <script>\n",
       "        \n",
       "        /**\n",
       "        * Simulation Viewer Injector\n",
       "        *\n",
       "        * Monitors the document for elements being added in the form:\n",
       "        *\n",
       "        *    <div class=\"simulation-viewer\" data-width=\"800\" data-height=\"800\" data-simulation=\"{...}\" />\n",
       "        *\n",
       "        * This script will then inject an iframe to the viewer application, and pass it the simulation data\n",
       "        * via the postMessage API on request. The script may be safely included multiple times, with only the\n",
       "        * configuration of the first started script (e.g. viewer URL) applying.\n",
       "        *\n",
       "        */\n",
       "        (function() {\n",
       "            const TARGET_CLASS = \"simulation-viewer\";\n",
       "            const ACTIVE_CLASS = \"simulation-viewer-active\";\n",
       "            const VIEWER_URL = \"https://feature-simulation-viewer.d3a9gfg7glllfq.amplifyapp.com/simulation-viewer\";\n",
       "\n",
       "            class SimulationViewerInjector {\n",
       "                constructor() {\n",
       "                    for (var node of document.getElementsByClassName(TARGET_CLASS)) {\n",
       "                        this.injectViewer(node);\n",
       "                    }\n",
       "\n",
       "                    // Monitor for newly added nodes to the DOM\n",
       "                    this.observer = new MutationObserver(this.onMutations.bind(this));\n",
       "                    this.observer.observe(document.body, {childList: true, subtree: true});\n",
       "                }\n",
       "\n",
       "                onMutations(mutations) {\n",
       "                    for (var mutation of mutations) {\n",
       "                        if (mutation.type === 'childList') {\n",
       "                            /**\n",
       "                            * Have found that adding the element does not reliably trigger the mutation observer.\n",
       "                            * It may be the case that setting content with innerHTML does not trigger.\n",
       "                            *\n",
       "                            * It seems to be sufficient to re-scan the document for un-activated viewers\n",
       "                            * whenever an event occurs, as Jupyter triggers multiple events on cell evaluation.\n",
       "                            */\n",
       "                            var viewers = document.getElementsByClassName(TARGET_CLASS);\n",
       "                            for (var node of viewers) {\n",
       "                                this.injectViewer(node);\n",
       "                            }\n",
       "                        }\n",
       "                    }\n",
       "                }\n",
       "\n",
       "                injectViewer(node) {\n",
       "                    // (re-)check that this is a valid simulation container and has not already been injected\n",
       "                    if (node.classList.contains(TARGET_CLASS) && !node.classList.contains(ACTIVE_CLASS)) {\n",
       "                        // Mark node as injected, to prevent re-runs\n",
       "                        node.classList.add(ACTIVE_CLASS);\n",
       "\n",
       "                        var uuid;\n",
       "                        if (window.crypto && window.crypto.randomUUID) {\n",
       "                            uuid = window.crypto.randomUUID();\n",
       "                        } else {\n",
       "                            uuid = \"\" + Math.random();\n",
       "                        }\n",
       "\n",
       "                        var frame = document.createElement(\"iframe\");\n",
       "                        frame.width = node.dataset.width || 800;\n",
       "                        frame.height = node.dataset.height || 800;\n",
       "                        frame.src = VIEWER_URL + \"?uuid=\" + uuid;\n",
       "\n",
       "                        var postMessageToViewer;\n",
       "                        postMessageToViewer = event => {{\n",
       "                            if(event.data.type === 'viewer' && event.data.uuid===uuid){{\n",
       "                                var simulation = JSON.parse(node.dataset.simulation);\n",
       "                                frame.contentWindow.postMessage({ type: 'jupyter', uuid, value: simulation}, '*');\n",
       "\n",
       "                                // Run once only\n",
       "                                window.removeEventListener('message', postMessageToViewer);\n",
       "                            }}\n",
       "                        }};\n",
       "                        window.addEventListener(\n",
       "                            'message',\n",
       "                            postMessageToViewer,\n",
       "                            false\n",
       "                        );\n",
       "\n",
       "                        node.appendChild(frame);\n",
       "                    }\n",
       "                }\n",
       "            }\n",
       "\n",
       "            if (!window.simulationViewerInjector) {\n",
       "                window.simulationViewerInjector = new SimulationViewerInjector();\n",
       "            }\n",
       "        })();\n",
       "    \n",
       "    </script>\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ellipse_TE.plot_3d()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# shift the plot positions a little so the field monitors don't overlap plots\n",
    "shift_plot = 0.01\n",
    "f, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 4), tight_layout=True)\n",
    "ellipse_TE.plot(x=0.0 + shift_plot, ax=ax1)\n",
    "ellipse_TE.plot(z=center_metal_z + shift_plot, ax=ax2)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Submit the Simulation and Visualize the Result\n",
    "\n",
    "Now we run the simulation and plot the absorption spectrum first."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">16:45:23 UTC </span>Created task <span style=\"color: #008000; text-decoration-color: #008000\">'thermal_emitter_ellipse'</span> with task_id                \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'fdve-d9e0e6c5-828e-4fb7-89fc-2270087c509a'</span> and task_type <span style=\"color: #008000; text-decoration-color: #008000\">'FDTD'</span>.  \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m16:45:23 UTC\u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'thermal_emitter_ellipse'\u001b[0m with task_id                \n",
       "\u001b[2;36m             \u001b[0m\u001b[32m'fdve-d9e0e6c5-828e-4fb7-89fc-2270087c509a'\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-d9e0e6c5-828e-4fb7-89fc-2270087c509a\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'https://tidy3d.simulation.cloud/workbench?taskId=fdve-d9e0e6c5-828</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-d9e0e6c5-828e-4fb7-89fc-2270087c509a\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">e-4fb7-89fc-2270087c509a'</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=706788;https://tidy3d.simulation.cloud/workbench?taskId=fdve-d9e0e6c5-828e-4fb7-89fc-2270087c509a\u001b\\\u001b[32m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=546295;https://tidy3d.simulation.cloud/workbench?taskId=fdve-d9e0e6c5-828e-4fb7-89fc-2270087c509a\u001b\\\u001b[32mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=706788;https://tidy3d.simulation.cloud/workbench?taskId=fdve-d9e0e6c5-828e-4fb7-89fc-2270087c509a\u001b\\\u001b[32m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=573837;https://tidy3d.simulation.cloud/workbench?taskId=fdve-d9e0e6c5-828e-4fb7-89fc-2270087c509a\u001b\\\u001b[32mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=706788;https://tidy3d.simulation.cloud/workbench?taskId=fdve-d9e0e6c5-828e-4fb7-89fc-2270087c509a\u001b\\\u001b[32m-d9e0e6c5-828\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=706788;https://tidy3d.simulation.cloud/workbench?taskId=fdve-d9e0e6c5-828e-4fb7-89fc-2270087c509a\u001b\\\u001b[32me-4fb7-89fc-2270087c509a'\u001b[0m\u001b]8;;\u001b\\.                                         \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e34947144d4b473bbd3dcbadd04e0484",
       "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\">16:45:24 UTC </span>status = success                                                   \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m16:45:24 UTC\u001b[0m\u001b[2;36m \u001b[0mstatus = success                                                   \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0097b75ec2bc43fc9c1e7e3431ebc2d6",
       "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\">             </span>loading simulation from simulation_data.hdf5                       \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mloading simulation from simulation_data.hdf5                       \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sim_data = web.run(ellipse_TE, task_name=\"thermal_emitter_ellipse\", verbose=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The absorption spectrum (1 - reflection) shows one narrow peak, indicating a high Q resonance. The result reproduces Fig.1 of the [reference](https://doi.org/10.1364/OPTICA.514203)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "wavelengh = td.C_0 / spectrum_freq\n",
    "reflection = sim_data[\"reflection\"].flux\n",
    "plt.plot(wavelengh, 1.0 - reflection, c='red', linewidth=2)\n",
    "plt.xlabel(\"Wavelength ($\\mu m$)\")\n",
    "plt.ylabel(\"Absorption\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally we plot the field distributions from the field monitors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sim_data.plot_field(\"field_xy\", \"Ex\", \"real\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sim_data.plot_field(\"field_xz\", \"E\", \"abs\")\n",
    "ax.set_ylim(-2,3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "applications": [
   "Metamaterials, gratings, and other periodic structures"
  ],
  "description": "This notebook demonstrates the narrowband mir-IR thermal emitters via quasi-BIC metasurfaces.",
  "feature_image": "./img/ellipse_thermal_emitter.png",
  "features": [],
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "keywords": "metasurface, mir-ir, quasi-BIC, nanophotonics, 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"
  },
  "redirect_from": "/tidy3d/xMqdtkiMqW",
  "title": "Narrowband Mid-IR Thermal Emitters via quasi-BIC Metasurfaces | Flexcompute"
 },
 "nbformat": 4,
 "nbformat_minor": 4
}