{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# High-Q silicon resonator\n",
        "\n",
        "In this example, we reproduce the findings of Zhang et al. (2018), which\n",
        "is linked\n",
        "[here](https://www.osapublishing.org/ol/abstract.cfm?uri=ol-43-8-1842).\n",
        "\n",
        "This notebook was originally developed and written by Romil Audhkhasi\n",
        "(USC).\n",
        "\n",
        "The paper investigates the resonances of silicon structures by measuring\n",
        "their transmission spectrum under varying geometric parameters.\n",
        "\n",
        "The paper uses a commercial finite element solver , which matches the\n",
        "result from Tidy3D.\n",
        "\n",
        "<img src=\"img/Si_struct.png\" alt=\"diagram\" width=\"500\"/>\n",
        "\n",
        "(Citation: Opt. Lett. 43, 1842-1845 (2018). With permission from the\n",
        "Optical Society)\n",
        "\n",
        "To do this calculation, we use a broadband pulse and frequency monitor\n",
        "to measure the flux on the opposite side of the structure.\n",
        "\n",
        "Please check out another case study where we investigated a high-Q\n",
        "[Germanium Fano\n",
        "metasurface](https://www.flexcompute.com/tidy3d/examples/notebooks/HighQGe/)."
      ],
      "id": "d4584844-739f-45e9-b5ea-1aefd31bbb3e"
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {},
      "outputs": [],
      "source": [
        "# standard python imports\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "# tidy3D import\n",
        "import tidy3d as td\n",
        "from tidy3d import web"
      ],
      "id": "53091160"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Set Up Simulation"
      ],
      "id": "b50749f6-7900-4800-adfb-611b174443ea"
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {},
      "outputs": [],
      "source": [
        "nm = 1e-3\n",
        "\n",
        "# define the frequencies we want to measure\n",
        "Nfreq = 1000\n",
        "wavelengths = nm * np.linspace(1050, 1400, Nfreq)\n",
        "freqs = td.constants.C_0 / wavelengths\n",
        "\n",
        "# define the frequency center and width of our pulse\n",
        "freq0 = freqs[len(freqs) // 2]\n",
        "freqw = freqs[0] - freqs[-1]\n",
        "\n",
        "# Define material properties\n",
        "n_SiO2 = 1.46\n",
        "n_Si = 3.52\n",
        "SiO2 = td.Medium(permittivity=n_SiO2**2)\n",
        "Si = td.Medium(permittivity=n_Si**2)"
      ],
      "id": "cdd19784"
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {},
      "outputs": [],
      "source": [
        "# space between resonators and source\n",
        "spc = 1.5\n",
        "\n",
        "# geometric parameters\n",
        "Px = Py = P = 650 * nm  # periodicity in x and y\n",
        "t = 260 * nm  # thickness of silicon\n",
        "g = 80 * nm  # gap size\n",
        "L = 480 * nm  # length in x\n",
        "w_sum = 400 * nm  # sum of lengths in y\n",
        "\n",
        "# resolution (should be commensurate with periodicity)\n",
        "dl = P / 32\n",
        "\n",
        "\n",
        "# computes widths in y (w1 and w2) given the difference in lengths in y and the sum of lengths\n",
        "def calc_ws(delta):\n",
        "    \"\"\"delta is a tunable parameter used to break symmetry.\n",
        "    w_sum = w1 + w2\n",
        "    delta = w1 - w2\n",
        "    w_sum + delta = 2 * w1\n",
        "    \"\"\"\n",
        "    w1 = (w_sum + delta) / 2\n",
        "    w2 = w_sum - w1\n",
        "    return w1, w2"
      ],
      "id": "f61873f9"
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {},
      "outputs": [],
      "source": [
        "# total size in z and [x,y,z]\n",
        "Lz = spc + t + t + spc\n",
        "sim_size = [Px, Py, Lz]\n",
        "\n",
        "# sio2 substrate\n",
        "substrate = td.Structure(\n",
        "    geometry=td.Box(\n",
        "        center=[0, 0, -Lz / 2],\n",
        "        size=[td.inf, td.inf, 2 * (spc + t)],\n",
        "    ),\n",
        "    medium=SiO2,\n",
        "    name=\"substrate\",\n",
        ")\n",
        "\n",
        "\n",
        "# creates a list of structures given a value of 'delta'\n",
        "def geometry(delta):\n",
        "    w1, w2 = calc_ws(delta)\n",
        "    center_y = (w1 - w2) / 2.0\n",
        "\n",
        "    cell1 = td.Structure(\n",
        "        geometry=td.Box(\n",
        "            center=[0, center_y + (g + w1) / 2.0, t / 2.0],\n",
        "            size=[L, w1, t],\n",
        "        ),\n",
        "        medium=Si,\n",
        "        name=\"cell1\",\n",
        "    )\n",
        "\n",
        "    cell2 = td.Structure(\n",
        "        geometry=td.Box(\n",
        "            center=[0, center_y - (g + w2) / 2.0, t / 2.0],\n",
        "            size=[L, w2, t],\n",
        "        ),\n",
        "        medium=Si,\n",
        "        name=\"cell2\",\n",
        "    )\n",
        "\n",
        "    return [substrate, cell1, cell2]"
      ],
      "id": "ccfb953f"
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {},
      "outputs": [],
      "source": [
        "# time dependence of source\n",
        "gaussian = td.GaussianPulse(freq0=freq0, fwidth=freqw)\n",
        "\n",
        "# plane wave source\n",
        "source = td.PlaneWave(\n",
        "    source_time=gaussian,\n",
        "    direction=\"-\",\n",
        "    size=(td.inf, td.inf, 0),\n",
        "    center=(0, 0, Lz / 2 - spc + 2 * dl),\n",
        "    pol_angle=0.0,\n",
        ")\n",
        "\n",
        "# Simulation run time.  Note you need to run a long time to calculate high Q resonances.\n",
        "run_time = 7e-12"
      ],
      "id": "d1621cd5"
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {},
      "outputs": [],
      "source": [
        "# monitor fields on other side of structure (substrate side) at range of frequencies\n",
        "monitor = td.FluxMonitor(\n",
        "    center=[0.0, 0.0, -Lz / 2 + spc - 2 * dl],\n",
        "    size=[td.inf, td.inf, 0],\n",
        "    freqs=freqs,\n",
        "    name=\"flux\",\n",
        ")"
      ],
      "id": "29af501a"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Define Case Studies\n",
        "\n",
        "Here we define the three simulations to run\n",
        "\n",
        "- With no resonators (normalization)\n",
        "- With symmetric (delta = 0) resonators\n",
        "- With asymmetric (delta != 0) resonators"
      ],
      "id": "9ec8d372-75d9-4fbc-b1b2-49a9e2ad2fe8"
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {},
      "outputs": [],
      "source": [
        "grid_spec = td.GridSpec(\n",
        "    grid_x=td.UniformGrid(dl=dl),\n",
        "    grid_y=td.UniformGrid(dl=dl),\n",
        "    grid_z=td.AutoGrid(min_steps_per_wvl=32),\n",
        ")\n",
        "\n",
        "# normalizing run (no Si) to get baseline transmission vs freq\n",
        "# can be run for shorter time as there are no resonances\n",
        "sim_empty = td.Simulation(\n",
        "    size=sim_size,\n",
        "    grid_spec=grid_spec,\n",
        "    structures=[substrate],\n",
        "    sources=[source],\n",
        "    monitors=[monitor],\n",
        "    run_time=run_time / 10,\n",
        "    boundary_spec=td.BoundarySpec(\n",
        "        x=td.Boundary.periodic(), y=td.Boundary.periodic(), z=td.Boundary.pml()\n",
        "    ),\n",
        ")\n",
        "\n",
        "# run with delta = 0\n",
        "sim_d0 = td.Simulation(\n",
        "    size=sim_size,\n",
        "    grid_spec=grid_spec,\n",
        "    structures=geometry(0),\n",
        "    sources=[source],\n",
        "    monitors=[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",
        ")\n",
        "\n",
        "# run with delta = 20nm\n",
        "sim_d20 = td.Simulation(\n",
        "    size=sim_size,\n",
        "    grid_spec=grid_spec,\n",
        "    structures=geometry(20 * nm),\n",
        "    sources=[source],\n",
        "    monitors=[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",
        ")"
      ],
      "id": "4460bc14"
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {},
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
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wGXd9WUtQsUHhis5Mw0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          }
        }
      ],
      "source": [
        "# Structure visualization in various planes\n",
        "\n",
        "fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(14, 8))\n",
        "sim_d0.plot_eps(x=0, ax=ax1)\n",
        "sim_d0.plot_eps(y=g, ax=ax2)\n",
        "sim_d0.plot_eps(z=0, ax=ax3)\n",
        "plt.show()"
      ],
      "id": "72bed1c0"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Run Simulations"
      ],
      "id": "df8b4a55-f120-42d8-aaf2-f21616b4044a"
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {},
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<script type=\"application/vnd.jupyter.widget-view+json\">\n",
              "{\"model_id\":\"913ebb2cab3b4829a4314a81d2b4474e\",\"version_major\":2,\"version_minor\":0,\"quarto_mimetype\":\"application/vnd.jupyter.widget-view+json\"}\n",
              "</script>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "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>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "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\">05:01:20 UTC </span>Started working on Batch containing <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3</span> tasks.                       \n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "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\">05:01:23 UTC </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.075</span> for the whole batch.                \n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "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>completion.                                                        \n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<script type=\"application/vnd.jupyter.widget-view+json\">\n",
              "{\"model_id\":\"c994bf6a71534ae2b5cbf54238c8b501\",\"version_major\":2,\"version_minor\":0,\"quarto_mimetype\":\"application/vnd.jupyter.widget-view+json\"}\n",
              "</script>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "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\">05:01:24 UTC </span>Batch complete.                                                    \n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "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>"
            ]
          }
        }
      ],
      "source": [
        "batch = web.Batch(\n",
        "    simulations={\n",
        "        \"normalization\": sim_empty,\n",
        "        \"Si-resonator-delta-0\": sim_d0,\n",
        "        \"Si-resonator-delta-20\": sim_d20,\n",
        "    },\n",
        "    verbose=True,\n",
        ")\n",
        "\n",
        "results = batch.run(path_dir=\"data\")"
      ],
      "id": "ef52c3df"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Get Results and Plot"
      ],
      "id": "038b6c95-19ca-40bf-ac43-1a230a956638"
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {},
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "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\">05:01:25 UTC </span><span style=\"color: #800000; text-decoration-color: #800000\">WARNING: Simulation final field decay value of </span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.00121</span><span style=\"color: #800000; text-decoration-color: #800000\"> is greater  </span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">than the simulation shutoff threshold of </span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1e-05</span><span style=\"color: #800000; text-decoration-color: #800000\">. Consider running   </span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">the simulation again with a larger </span><span style=\"color: #008000; text-decoration-color: #008000\">'run_time'</span><span style=\"color: #800000; text-decoration-color: #800000\"> duration for more    </span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">accurate results.                                                  </span>\n",
              "</pre>"
            ]
          }
        }
      ],
      "source": [
        "batch_data = batch.load(path_dir=\"data\")\n",
        "flux_norm = batch_data[\"normalization\"][\"flux\"].flux\n",
        "trans_g0 = batch_data[\"Si-resonator-delta-0\"][\"flux\"].flux / flux_norm\n",
        "trans_g20 = batch_data[\"Si-resonator-delta-20\"][\"flux\"].flux / flux_norm"
      ],
      "id": "1582d985"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "The normalizing run computes the transmitted flux for an air -\\> SiO2\n",
        "interface, which is just below unity due to some reflection.\n",
        "\n",
        "While not technically necessary for this example, since this\n",
        "transmission can be computed analytically, it is often a good idea to\n",
        "run a normalizing run so you can accurately measure the *change* in\n",
        "output when the structure is added. For example, for multilayer\n",
        "structures, the normalizing run displays frequency dependence, which\n",
        "would make it prudent to include in the calculation."
      ],
      "id": "929dee62-9653-4846-b4f3-9ed5e4ccd14a"
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {},
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "image/png": 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lvZONcQooJKr5scTDW/UEaImHiIiIRD+nE3r3hjfeiPRMwksBhQQtNRUuvhhmzICKFT2/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kyZgtPppE6dOnz55ZfUqVPHjtuKSBiogiK02rUzP166NPhr2fWplMNhzHvLFmOnjy1bYOdO6NgR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\nAFDcDzoSvC4EqF6HAAAAAElFTkSuQmCC\n"
          }
        }
      ],
      "source": [
        "# plot transmission, compare to paper results, look similar\n",
        "fig, ax = plt.subplots(1, 1, figsize=(6, 4.5))\n",
        "wavelengths_nm = td.C_0 / trans_g0.f / nm\n",
        "plt.plot(wavelengths_nm, trans_g0.values, color=\"red\", label=r\"$\\delta=0$\")\n",
        "plt.plot(wavelengths_nm, trans_g20.values, color=\"blue\", label=r\"$\\delta=20~nm$\")\n",
        "plt.xlabel(\"wavelength ($nm$)\")\n",
        "plt.ylabel(\"Transmission\")\n",
        "plt.xlim([1050, 1400])\n",
        "plt.ylim([0, 1])\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "id": "5c0bd3e6"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Results Comparison\n",
        "\n",
        "Compare this plot to published results:\n",
        "\n",
        "<img src=\"img/Si_plot.png\" alt=\"diagram\" width=\"400\"/>\n",
        "\n",
        "(Citation: Opt. Lett. 43, 1842-1845 (2018). With permission from the\n",
        "Optical Society)\n",
        "\n",
        "Besides the metasurface demonstrated in this notebook, in Tidy3D’s\n",
        "example library we have demonstrated a [dielectric metasurface\n",
        "absorber](https://www.flexcompute.com/tidy3d/examples/notebooks/DielectricMetasurfaceAbsorber/),\n",
        "a [gradient metasurface\n",
        "reflector](https://www.flexcompute.com/tidy3d/examples/notebooks/GradientMetasurfaceReflector/),\n",
        "a [metalens at the visible\n",
        "frequency](https://www.flexcompute.com/tidy3d/examples/notebooks/Metalens/),\n",
        "and a [graphene metamaterial\n",
        "absorber](https://www.flexcompute.com/tidy3d/examples/notebooks/GrapheneMetamaterial/)."
      ],
      "id": "4be9cc80-1435-4ee0-8152-e4abf12399b5"
    }
  ],
  "nbformat": 4,
  "nbformat_minor": 5,
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
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}