{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Resonator benchmark (Lumerical)\n", "\n", "In this example, we reproduce the findings of Campione et al. (2016), which is linked [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 (USC). \n", "\n", "The paper investigates the resonances of Germanium structures by measuring their transmission spectrum under varying geometric parameters.\n", "\n", "The paper uses a finite-difference time-domain (Lumerical), which matches the result from Tidy3D.\n", "\n", "\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." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "execution": { "iopub.execute_input": "2023-02-03T06:17:45.095538Z", "iopub.status.busy": "2023-02-03T06:17:45.095284Z", "iopub.status.idle": "2023-02-03T06:17:45.766646Z", "shell.execute_reply": "2023-02-03T06:17:45.766229Z" }, "id": "8WfOAGgkyh3K", "outputId": "f02b3adf-9117-42b1-a69c-b5f4f3f0a6c5", "tags": [] }, "outputs": [ { "data": { "text/html": [ "
[00:17:45] 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", "\n" ], "text/plain": [ "\u001b[2;36m[00:17:45]\u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING \u001b[0m This version of Tidy3D was pip installed from the \u001b[32m'tidy3d-beta'\u001b[0m repository on \u001b]8;id=851470;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=103135;file:///Users/twhughes/Documents/Flexcompute/tidy3d-docs/tidy3d/tidy3d/__init__.py#103\u001b\\\u001b[2m103\u001b[0m\u001b]8;;\u001b\\\n", "\u001b[2;36m \u001b[0m PyPI. Future releases will be uploaded to the \u001b[32m'tidy3d'\u001b[0m repository. From now on, \u001b[2m \u001b[0m\n", "\u001b[2;36m \u001b[0m please use \u001b[32m'pip install tidy3d'\u001b[0m instead. \u001b[2m \u001b[0m\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
INFO Using client version: 1.9.0rc1 __init__.py:121\n", "\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=66376;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=671102;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": [ "# standard python imports\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "# tidy3D import\n", "import tidy3d as td\n", "from tidy3d import web\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Set Up Simulation" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "execution": { "iopub.execute_input": "2023-02-03T06:17:45.768448Z", "iopub.status.busy": "2023-02-03T06:17:45.768286Z", "iopub.status.idle": "2023-02-03T06:17:45.770801Z", "shell.execute_reply": "2023-02-03T06:17:45.770517Z" }, "id": "gy7J23c5yh3S", "tags": [] }, "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)\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "execution": { "iopub.execute_input": "2023-02-03T06:17:45.772296Z", "iopub.status.busy": "2023-02-03T06:17:45.772179Z", "iopub.status.idle": "2023-02-03T06:17:45.774162Z", "shell.execute_reply": "2023-02-03T06:17:45.773869Z" }, "id": "mGFiHbvhyh3T", "tags": [] }, "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\n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "execution": { "iopub.execute_input": "2023-02-03T06:17:45.775527Z", "iopub.status.busy": "2023-02-03T06:17:45.775428Z", "iopub.status.idle": "2023-02-03T06:17:45.778327Z", "shell.execute_reply": "2023-02-03T06:17:45.778059Z" }, "id": "Ol9GwSGUyh3U", "tags": [] }, "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", ")\n" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "execution": { "iopub.execute_input": "2023-02-03T06:17:45.779592Z", "iopub.status.busy": "2023-02-03T06:17:45.779498Z", "iopub.status.idle": "2023-02-03T06:17:45.781635Z", "shell.execute_reply": "2023-02-03T06:17:45.781383Z" }, "id": "YO2yKasKyh3U", "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", " 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 = 3e-11\n" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "execution": { "iopub.execute_input": "2023-02-03T06:17:45.782958Z", "iopub.status.busy": "2023-02-03T06:17:45.782865Z", "iopub.status.idle": "2023-02-03T06:17:45.784860Z", "shell.execute_reply": "2023-02-03T06:17:45.784589Z" }, "id": "4TZn_s8kyh3V", "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", ")\n" ] }, { "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\n" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "execution": { "iopub.execute_input": "2023-02-03T06:17:45.786245Z", "iopub.status.busy": "2023-02-03T06:17:45.786150Z", "iopub.status.idle": "2023-02-03T06:17:45.790866Z", "shell.execute_reply": "2023-02-03T06:17:45.790578Z" }, "id": "fnpi5BiZyh3W", "outputId": "d25c2f44-d5a2-4241-b69b-0ffe5369ec5b", "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 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(x=td.Boundary.periodic(), y=td.Boundary.periodic(), z=td.Boundary.pml()),\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(x=td.Boundary.periodic(), y=td.Boundary.periodic(), z=td.Boundary.pml()),\n", ")\n" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 513 }, "execution": { "iopub.execute_input": "2023-02-03T06:17:45.792271Z", "iopub.status.busy": "2023-02-03T06:17:45.792188Z", "iopub.status.idle": "2023-02-03T06:17:46.051272Z", "shell.execute_reply": "2023-02-03T06:17:46.051009Z" }, "id": "T8YFcftUyh3X", "outputId": "93b60986-ceb3-4064-eb8d-71a75495c11d", "tags": [] }, "outputs": [ { "data": { "image/png": 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\n", 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