{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Germanium Fano metasurface\n",
        "\n",
        "In this example, we reproduce the findings of Campione et al. (2016),\n",
        "which is linked\n",
        "[here](https://pubs.acs.org/doi/abs/10.1021/acsphotonics.6b00556?casa_token=v7Cq9VMW40UAAAAA:AWrBfYCHrwGQ9PYBYgdprrQ8X8i-nOairIplRs1Ejo2sbDmFT9nsV1M6UEXpvOfYSnwMjagD9IT97Ph2).\n",
        "\n",
        "This notebook was originally developed and written by Romil Audhkhasi\n",
        "(USC).\n",
        "\n",
        "The paper investigates the resonances of Germanium structures by\n",
        "measuring their transmission spectrum under varying geometric\n",
        "parameters.\n",
        "\n",
        "The paper uses a different commercial finite-difference time-domain\n",
        "software, which matches the result from Tidy3D."
      ],
      "id": "e6030c97-2e28-431b-b8d6-fab295ba84e1"
    },
    {
      "cell_type": "raw",
      "metadata": {
        "raw_mimetype": "text/html"
      },
      "source": [
        "<!-- <img src=\"img/Ge_struct.png\" alt=\"diagram\" width=\"300\"/> -->"
      ],
      "id": "d991837b-38dd-4c19-992b-75d22e1befa0"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "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",
        "[silicon Fano\n",
        "resonator](https://www.flexcompute.com/tidy3d/examples/notebooks/HighQSi/).\n",
        "For more simulation examples, please visit our [examples\n",
        "page](https://www.flexcompute.com/tidy3d/examples/). If you are new to\n",
        "the finite-difference time-domain (FDTD) method, we highly recommend\n",
        "going through our [FDTD101](https://www.flexcompute.com/fdtd101/)\n",
        "tutorials. FDTD simulations can diverge due to various reasons. If you\n",
        "run into any simulation divergence issues, please follow the steps\n",
        "outlined in our [troubleshooting\n",
        "guide](https://www.flexcompute.com/tidy3d/examples/notebooks/DivergedFDTDSimulation/)\n",
        "to resolve it."
      ],
      "id": "31a11bc7-7654-4c7c-9bda-7b282ce77935"
    },
    {
      "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": "d1537e14"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Set Up Simulation"
      ],
      "id": "4c5212d2-3302-439e-802a-fd7b826d4f5c"
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {},
      "outputs": [],
      "source": [
        "Nfreq = 1000\n",
        "wavelengths = np.linspace(8, 12, Nfreq)\n",
        "freqs = td.constants.C_0 / wavelengths\n",
        "freq0 = freqs[len(freqs) // 2]\n",
        "freqw = freqs[0] - freqs[-1]\n",
        "\n",
        "# Define material properties\n",
        "n_BaF2 = 1.45\n",
        "n_Ge = 4\n",
        "BaF2 = td.Medium(permittivity=n_BaF2**2)\n",
        "Ge = td.Medium(permittivity=n_Ge**2)"
      ],
      "id": "5f609066"
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {},
      "outputs": [],
      "source": [
        "# space between resonators and source\n",
        "spc = 8\n",
        "\n",
        "# geometric parameters\n",
        "Px = Py = P = 4.2\n",
        "h = 2.53\n",
        "L1 = 3.036\n",
        "L2 = 2.024\n",
        "w1 = w2 = w = 1.265\n",
        "\n",
        "# resolution (should be commensurate with periodicity)\n",
        "dl = P / 32"
      ],
      "id": "0265eb89"
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {},
      "outputs": [],
      "source": [
        "# total size in z and [x,y,z]\n",
        "Lz = spc + h + h + spc\n",
        "sim_size = [Px, Py, Lz]\n",
        "\n",
        "# BaF2 substrate\n",
        "substrate = td.Structure(\n",
        "    geometry=td.Box(\n",
        "        center=[0, 0, -Lz / 2],\n",
        "        size=[td.inf, td.inf, 2 * (spc + h)],\n",
        "    ),\n",
        "    medium=BaF2,\n",
        "    name=\"substrate\",\n",
        ")\n",
        "\n",
        "# Define structure\n",
        "\n",
        "cell1 = td.Structure(\n",
        "    geometry=td.Box(\n",
        "        center=[(L1 / 2) - L2, -w1 / 2, h / 2],\n",
        "        size=[L1, w1, h],\n",
        "    ),\n",
        "    medium=Ge,\n",
        "    name=\"cell1\",\n",
        ")\n",
        "\n",
        "cell2 = td.Structure(\n",
        "    geometry=td.Box(\n",
        "        center=[-L2 / 2, w2 / 2, h / 2],\n",
        "        size=[L2, w2, h],\n",
        "    ),\n",
        "    medium=Ge,\n",
        "    name=\"cell2\",\n",
        ")"
      ],
      "id": "ee4997a3"
    },
    {
      "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",
        "    size=(td.inf, td.inf, 0),\n",
        "    center=(0, 0, Lz / 2 - spc + 2 * dl),\n",
        "    direction=\"-\",\n",
        "    pol_angle=0,\n",
        ")\n",
        "\n",
        "# Simulation run time.  Note you need to run a long time to calculate high Q resonances.\n",
        "run_time = 8e-11"
      ],
      "id": "48914d36"
    },
    {
      "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": "57f4bc06"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Define Case Studies\n",
        "\n",
        "Here we define the two simulations to run\n",
        "\n",
        "- With no resonator (normalization)\n",
        "- With Ge resonator"
      ],
      "id": "f4af983d-3e39-4d12-9e43-d495d705567f"
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "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\">04:58:28 UTC </span><span style=\"color: #800000; text-decoration-color: #800000\">WARNING: RF simulations and functionality will require new license </span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">requirements in an upcoming release. All RF-specific classes are   </span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">now available within the sub-package </span><span style=\"color: #008000; text-decoration-color: #008000\">'tidy3d.rf'</span><span style=\"color: #800000; text-decoration-color: #800000\">.                  </span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\"> - Contains sources defined for RF wavelengths.                    </span>\n",
              "</pre>"
            ]
          }
        }
      ],
      "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 Ge) 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 Ge nanorod\n",
        "sim_actual = td.Simulation(\n",
        "    size=sim_size,\n",
        "    grid_spec=td.GridSpec.uniform(dl=dl),\n",
        "    structures=[substrate, cell1, cell2],\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": "91802c31"
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {},
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
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Lly52t2/dutV83qdPH4f5tWnTRmXLltXp06eVlpamv//+W82bNze3jxgxQq+9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\nAMClgr1dAQCAf5oyZYo6dOggSTp79qy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TjtLn9/trGTi2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1fr1q3T8ePH9fDDD1t8fIdMFO3evVvHjx8v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\npp9++smU/JDyEwCTJk1yySSRJL3xxhtav369Bg4cqLp168rb2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          }
        }
      ],
      "source": [
        "# Structure visualization in various planes\n",
        "\n",
        "fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(10, 6))\n",
        "sim_actual.plot_eps(x=0, ax=ax1)\n",
        "sim_actual.plot_eps(y=-0.1, ax=ax2)\n",
        "sim_actual.plot_eps(z=0.1, ax=ax3)\n",
        "plt.show()"
      ],
      "id": "69209f4d"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Run Simulations"
      ],
      "id": "12068d80-32b0-42ae-bf12-d6a128baf13c"
    },
    {
      "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\":\"372eecb958464381a321cdfde90fe3be\",\"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\">04:58:31 UTC </span>Started working on Batch containing <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2</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\">04:58:33 UTC </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.050</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\":\"af6697ff9f3a4a6ca0e1ab2fbef7235d\",\"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\">04:58:34 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": [
        "# run all simulations, take about 2-3 minutes each with some download time\n",
        "batch = web.Batch(simulations={\"norm\": sim_empty, \"actual\": sim_actual}, verbose=True)\n",
        "batch_data = batch.run(path_dir=\"data\")"
      ],
      "id": "717ffad0"
    },
    {
      "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": "4748ab99-8545-4e62-9653-5d2a21a2092f"
    },
    {
      "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\">04:58:35 UTC </span><span style=\"color: #800000; text-decoration-color: #800000\">WARNING: Warning messages were found in the solver log. For more   </span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">information, check </span><span style=\"color: #008000; text-decoration-color: #008000\">'SimulationData.log'</span><span style=\"color: #800000; text-decoration-color: #800000\"> or use                     </span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'web.download_log(task_id)'</span><span style=\"color: #800000; text-decoration-color: #800000\">.                                       </span>\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><span style=\"color: #800000; text-decoration-color: #800000\">WARNING: Warning messages were found in the solver log. For more   </span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">information, check </span><span style=\"color: #008000; text-decoration-color: #008000\">'SimulationData.log'</span><span style=\"color: #800000; text-decoration-color: #800000\"> or use                     </span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'web.download_log(task_id)'</span><span style=\"color: #800000; text-decoration-color: #800000\">.                                       </span>\n",
              "</pre>"
            ]
          }
        }
      ],
      "source": [
        "batch_data = batch.load(path_dir=\"data\")\n",
        "transmission = batch_data[\"actual\"][\"flux\"].flux / batch_data[\"norm\"][\"flux\"].flux\n",
        "reflection = 1 - transmission"
      ],
      "id": "c1292fca"
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {},
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "image/png": 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y5ElRfefOnejZs6dEXRERGTZj+cxncoGEKTCWb0599ezZM3zyySe4e/euyrnOnTtjzZo1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\nqlKlSjp69GiJHQe+JScnuypQkpKSGIEYfvFeQWHwf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          }
        }
      ],
      "source": [
        "# plot transmission, compare to paper results, look similar\n",
        "fig, ax = plt.subplots(1, 1, figsize=(6, 4.5))\n",
        "plt.plot(wavelengths, reflection, \"k\", label=\"R\")\n",
        "plt.plot(wavelengths, transmission, \"r--\", label=\"T\")\n",
        "plt.xlabel(r\"wavelength ($\\mu m$)\")\n",
        "plt.ylabel(\"Magnitude\")\n",
        "plt.xlim([8.8, 12])\n",
        "plt.ylim([0.0, 1.0])\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "id": "11ed1af9"
    },
    {
      "cell_type": "raw",
      "metadata": {
        "raw_mimetype": "text/html"
      },
      "source": [
        "<!-- <img src=\"img/Ge_plot.png\" alt=\"diagram\" width=\"300\"/> -->"
      ],
      "id": "e199dad4-02a9-457e-a20d-95f6924d1016"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "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": "98258965-472d-4fb0-a862-25cf8b6a8ec0"
    }
  ],
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