{ "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 is linked [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 (USC). \n", "\n", "The paper investigates the resonances of silicon structures by measuring their transmission spectrum under varying geometric parameters.\n", "\n", "The paper uses a commercial finite element solver , which matches the result from Tidy3D.\n", "\n", "\"diagram\"\n", "\n", "(Citation: Opt. Lett. 43, 1842-1845 (2018). With permission from the Optical Society)\n", "\n", "To do this calculation, we use a broadband pulse and frequency monitor 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 [Germanium Fano metasurface](https://www.flexcompute.com/tidy3d/examples/notebooks/HighQGe/)." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "tags": [] }, "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" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Set Up Simulation" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "tags": [] }, "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)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "tags": [] }, "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" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "tags": [] }, "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]" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "tags": [] }, "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" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "tags": [] }, "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", ")" ] }, { "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\n" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "tags": [] }, "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", ")" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "tags": [] }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "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()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Run Simulations" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "tags": [] }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "d6f50679e35f4461b2bf1208be1f134a", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Output()" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
09:59:47 UTC Started working on Batch containing 3 tasks.                       \n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m09:59:47 UTC\u001b[0m\u001b[2;36m \u001b[0mStarted working on Batch containing \u001b[1;36m3\u001b[0m tasks.                       \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
09:59:50 UTC Maximum FlexCredit cost: 0.075 for the whole batch.                \n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m09:59:50 UTC\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m0.075\u001b[0m for the whole batch.                \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
             Use 'Batch.real_cost()' to get the billed FlexCredit cost after the\n",
       "             Batch has completed.                                               \n",
       "
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      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mUse \u001b[32m'Batch.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m to get the billed FlexCredit cost after the\n",
       "\u001b[2;36m             \u001b[0mBatch has completed.                                               \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "81292258644d4f71868deef9b9774387",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
10:06:38 UTC Batch complete.                                                    \n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m10:06:38 UTC\u001b[0m\u001b[2;36m \u001b[0mBatch complete.                                                    \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "394bb9e1e7614b7bafa44bc43be92424",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "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\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get Results and Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "
10:06:42 UTC WARNING: 3 files have already been downloaded and will be skipped. \n",
       "             To forcibly overwrite existing files, invoke the load or download  \n",
       "             function with `replace_existing=True`.                             \n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m10:06:42 UTC\u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING: \u001b[0m\u001b[1;36m3\u001b[0m\u001b[31m files have already been downloaded and will be skipped. \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31mTo forcibly overwrite existing files, invoke the load or download  \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31mfunction with `\u001b[0m\u001b[33mreplace_existing\u001b[0m\u001b[31m=\u001b[0m\u001b[3;92mTrue\u001b[0m\u001b[31m`.                             \u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e776de4e9d744d208f3208cb01db4794",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
10:06:48 UTC WARNING: Simulation final field decay value of 0.00169 is greater  \n",
       "             than the simulation shutoff threshold of 1e-05. Consider running   \n",
       "             the simulation again with a larger 'run_time' duration for more    \n",
       "             accurate results.                                                  \n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m10:06:48 UTC\u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING: Simulation final field decay value of \u001b[0m\u001b[1;36m0.00169\u001b[0m\u001b[31m is greater  \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31mthan the simulation shutoff threshold of \u001b[0m\u001b[1;36m1e-05\u001b[0m\u001b[31m. Consider running   \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31mthe simulation again with a larger \u001b[0m\u001b[32m'run_time'\u001b[0m\u001b[31m duration for more    \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31maccurate results.                                                  \u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "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"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The normalizing run computes the transmitted flux for an air -> SiO2 interface, which is just below unity due to some reflection.\n",
    "\n",
    "While not technically necessary for this example, since this transmission can be computed analytically, it is often a good idea to run a normalizing run so you can accurately measure the *change* in output when the structure is added.  For example, for multilayer structures, the normalizing run displays frequency dependence, which would make it prudent to include in the calculation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "
"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "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()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Results Comparison\n",
    "\n",
    "Compare this plot to published results:\n",
    "\n",
    "\"diagram\"\n",
    "\n",
    "(Citation: Opt. Lett. 43, 1842-1845 (2018).  With permission from the Optical Society)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Besides the metasurface demonstrated in this notebook, in Tidy3D's example library we have demonstrated a [dielectric metasurface absorber](https://www.flexcompute.com/tidy3d/examples/notebooks/DielectricMetasurfaceAbsorber/), a [gradient metasurface reflector](https://www.flexcompute.com/tidy3d/examples/notebooks/GradientMetasurfaceReflector/), a [metalens at the visible frequency](https://www.flexcompute.com/tidy3d/examples/notebooks/Metalens/), and a [graphene metamaterial absorber](https://www.flexcompute.com/tidy3d/examples/notebooks/GrapheneMetamaterial/). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "applications": [
   "Metamaterials, gratings, and other periodic structures"
  ],
  "description": "This notebook demonstrates how to model a silicon resonator in Tidy3D FDTD.",
  "feature_image": "./img/high_q_silicon_feature_image.png",
  "features": [],
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "keywords": "high-Q, resonator, 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": "3.11.2"
  },
  "title": "Silicon Resonator Modeling in Tidy3D | Flexcompute",
  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "state": {
     "036fedd18fd14aa49fc25e546a0b6624": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
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