{ "cells": [ { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "-" } }, "source": [ "# Scattering matrix plugin\n", "\n", "Note: the cost of running the entire notebook is larger than 1 FlexUnit.\n", "\n", "This notebook will give a demo of the tidy3d [ComponentModeler](https://docs.flexcompute.com/projects/tidy3d/en/v1.9.0rc1/_autosummary/tidy3d.plugins.ComponentModeler.html) plugin used to compute scattering matrix elements." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "execution": { "iopub.execute_input": "2023-02-03T05:05:23.838156Z", "iopub.status.busy": "2023-02-03T05:05:23.837803Z", "iopub.status.idle": "2023-02-03T05:05:24.521948Z", "shell.execute_reply": "2023-02-03T05:05:24.521535Z" }, "tags": [] }, "outputs": [ { "data": { "text/html": [ "
[23:05:24] 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[23:05:24]\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=542209;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=180456;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=400427;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=192818;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": [ "# make sure notebook plots inline\n", "%matplotlib inline\n", "# standard python imports\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import os\n", "import gdstk\n", "\n", "# tidy3D imports\n", "import tidy3d as td\n", "from tidy3d import web\n", "\n", "# set tidy3d to only print error information to reduce verbosity\n", "td.config.logging_level = \"error\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Setup\n", "\n", "We will simulate a directional coupler, similar to the GDS and Parameter scan tutorials.\n", "\n", "Let's start by setting up some basic parameters." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "execution": { "iopub.execute_input": "2023-02-03T05:05:24.523791Z", "iopub.status.busy": "2023-02-03T05:05:24.523609Z", "iopub.status.idle": "2023-02-03T05:05:24.526923Z", "shell.execute_reply": "2023-02-03T05:05:24.526644Z" }, "tags": [] }, "outputs": [], "source": [ "# wavelength / frequency\n", "lambda0 = 1.550 # all length scales in microns\n", "freq0 = td.constants.C_0 / lambda0\n", "fwidth = freq0 / 10\n", "\n", "# Spatial grid specification\n", "grid_spec = td.GridSpec.auto(min_steps_per_wvl=14, wavelength=lambda0)\n", "\n", "# Permittivity of waveguide and substrate\n", "wg_n = 3.48\n", "sub_n = 1.45\n", "mat_wg = td.Medium(permittivity=wg_n**2)\n", "mat_sub = td.Medium(permittivity=sub_n**2)\n", "\n", "# Waveguide dimensions\n", "\n", "# Waveguide height\n", "wg_height = 0.22\n", "# Waveguide width\n", "wg_width = 1.0\n", "# Waveguide separation in the beginning/end\n", "wg_spacing_in = 8\n", "# length of coupling region (um)\n", "coup_length = 6.0\n", "# spacing between waveguides in coupling region (um)\n", "wg_spacing_coup = 0.05\n", "# Total device length along propagation direction\n", "device_length = 100\n", "# Length of the bend region\n", "bend_length = 16\n", "# Straight waveguide sections on each side\n", "straight_wg_length = 4\n", "# space between waveguide and PML\n", "pml_spacing = 2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Define waveguide bends and coupler\n", "\n", "Here is where we define our directional coupler shape programmatically in terms of the geometric parameters" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "execution": { "iopub.execute_input": "2023-02-03T05:05:24.528463Z", "iopub.status.busy": "2023-02-03T05:05:24.528370Z", "iopub.status.idle": "2023-02-03T05:05:24.531981Z", "shell.execute_reply": "2023-02-03T05:05:24.531701Z" }, "tags": [] }, "outputs": [], "source": [ "def tanh_interp(max_arg):\n", " \"\"\"Interpolator for tanh with adjustable extension\"\"\"\n", " scale = 1 / np.tanh(max_arg)\n", " return lambda u: 0.5 * (1 + scale * np.tanh(max_arg * (u * 2 - 1)))\n", "\n", "def make_coupler(\n", " length, wg_spacing_in, wg_width, wg_spacing_coup, coup_length, bend_length, npts_bend=30\n", "):\n", " \"\"\"Make an integrated coupler using the gdstk RobustPath object.\"\"\"\n", " # bend interpolator\n", " interp = tanh_interp(3)\n", " delta = wg_width + wg_spacing_coup - wg_spacing_in\n", " offset = lambda u: wg_spacing_in + interp(u) * delta\n", "\n", " coup = gdstk.RobustPath(\n", " (-0.5 * length, 0),\n", " (wg_width, wg_width),\n", " wg_spacing_in,\n", " simple_path=True,\n", " layer=1,\n", " datatype=[0, 1],\n", " )\n", " coup.segment((-0.5 * coup_length - bend_length, 0))\n", " coup.segment(\n", " (-0.5 * coup_length, 0), offset=[lambda u: -0.5 * offset(u), lambda u: 0.5 * offset(u)]\n", " )\n", " coup.segment((0.5 * coup_length, 0))\n", " coup.segment(\n", " (0.5 * coup_length + bend_length, 0),\n", " offset=[lambda u: -0.5 * offset(1 - u), lambda u: 0.5 * offset(1 - u)],\n", " )\n", " coup.segment((0.5 * length, 0))\n", " return coup" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Create Base Simulation\n", "\n", "The scattering matrix tool requires the \"base\" [Simulation](https://docs.flexcompute.com/projects/tidy3d/en/v1.9.0rc1/_autosummary/tidy3d.Simulation.html) (without the modal sources or monitors used to compute S-parameters), so we will construct that now.\n", "\n", "We generate the structures and add a [FieldMonitor](https://docs.flexcompute.com/projects/tidy3d/en/v1.9.0rc1/_autosummary/tidy3d.FieldMonitor.html?highlight=FieldMonitor) so we can inspect the field patterns." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "execution": { "iopub.execute_input": "2023-02-03T05:05:24.533333Z", "iopub.status.busy": "2023-02-03T05:05:24.533250Z", "iopub.status.idle": "2023-02-03T05:05:24.541998Z", "shell.execute_reply": "2023-02-03T05:05:24.541710Z" }, "tags": [] }, "outputs": [], "source": [ "# Geometry must be placed in GDS cells to import into Tidy3D\n", "coup_cell = gdstk.Cell(\"Coupler\")\n", "\n", "substrate = gdstk.rectangle(\n", " (-device_length / 2, -wg_spacing_in / 2 - 10),\n", " (device_length / 2, wg_spacing_in / 2 + 10),\n", " layer=0,\n", ")\n", "coup_cell.add(substrate)\n", "\n", "# Add the coupler to a gdspy cell\n", "gds_coup = make_coupler(\n", " device_length, wg_spacing_in, wg_width, wg_spacing_coup, coup_length, bend_length\n", ")\n", "coup_cell.add(gds_coup)\n", "\n", "# Substrate\n", "oxide_geo, = td.PolySlab.from_gds(\n", " gds_cell=coup_cell, gds_layer=0, gds_dtype=0, slab_bounds=(-10, 0), axis=2\n", ")\n", "\n", "oxide = td.Structure(geometry=oxide_geo, medium=mat_sub)\n", "\n", "# Waveguides (import all datatypes if gds_dtype not specified)\n", "coupler1_geo, coupler2_geo = td.PolySlab.from_gds(\n", " gds_cell=coup_cell, gds_layer=1, slab_bounds=(0, wg_height), axis=2\n", ")\n", "\n", "coupler1 = td.Structure(geometry=coupler1_geo, medium=mat_wg)\n", "\n", "coupler2 = td.Structure(geometry=coupler2_geo, medium=mat_wg)\n", "\n", "# Simulation size along propagation direction\n", "sim_length = 2 * straight_wg_length + 2 * bend_length + coup_length\n", "\n", "# Spacing between waveguides and PML\n", "sim_size = [\n", " sim_length,\n", " wg_spacing_in + wg_width + 2 * pml_spacing,\n", " wg_height + 2 * pml_spacing,\n", "]\n", "\n", "# source\n", "src_pos = sim_length / 2 - straight_wg_length / 2\n", "\n", "# in-plane field monitor (optional, increases required data storage)\n", "domain_monitor = td.FieldMonitor(\n", " center=[0, 0, wg_height / 2], size=[td.inf, td.inf, 0], freqs=[freq0], name=\"field\"\n", ")\n", "\n", "# initialize the simulation\n", "sim = td.Simulation(\n", " size=sim_size,\n", " grid_spec=grid_spec,\n", " structures=[oxide, coupler1, coupler2],\n", " sources=[],\n", " monitors=[domain_monitor],\n", " run_time=50 / fwidth,\n", " boundary_spec=td.BoundarySpec.all_sides(boundary=td.PML()),\n", ")" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "execution": { "iopub.execute_input": "2023-02-03T05:05:24.543418Z", "iopub.status.busy": "2023-02-03T05:05:24.543339Z", "iopub.status.idle": "2023-02-03T05:05:24.741952Z", "shell.execute_reply": "2023-02-03T05:05:24.741666Z" }, "tags": [] }, "outputs": [ { "data": { "image/png": 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