{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Automatic nonuniform grid\n", "\n", "Run this notebook in your browser using [Binder](https://mybinder.org/v2/gh/flexcompute-readthedocs/tidy3d-docs/readthedocs?labpath=docs%2Fsource%2Fnotebooks%2FAutoGrid.ipynb).\n", "\n", "Here we demonstrate how to use the automatic nonuniform grid generation available in Tidy3D. We will show different possible ways to discretize the air-slot waveguide first introduced in [this reference](https://opg.optica.org/ol/abstract.cfm?uri=ol-29-11-1209)." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "execution": { "iopub.execute_input": "2022-07-21T20:54:14.169796Z", "iopub.status.busy": "2022-07-21T20:54:14.169215Z", "iopub.status.idle": "2022-07-21T20:54:15.143507Z", "shell.execute_reply": "2022-07-21T20:54:15.143106Z" } }, "outputs": [], "source": [ "# basic imports\n", "import numpy as np\n", "import matplotlib.pylab as plt\n", "\n", "# tidy3d imports\n", "import tidy3d as td\n", "from tidy3d.plugins import ModeSolver" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "execution": { "iopub.execute_input": "2022-07-21T20:54:15.145521Z", "iopub.status.busy": "2022-07-21T20:54:15.145354Z", "iopub.status.idle": "2022-07-21T20:54:15.149523Z", "shell.execute_reply": "2022-07-21T20:54:15.149212Z" } }, "outputs": [], "source": [ "# basic parameters (note, all length units are microns)\n", "nm = 1e-3\n", "wavelength = 1550 * nm\n", "freq0 = td.C_0 / wavelength\n", "\n", "# waveguide parameters\n", "slot_width = 50 * nm\n", "box_length = 300 * nm\n", "box_height = 180 * nm\n", "\n", "# materials\n", "si_med = td.Medium(permittivity=3.48**2)\n", "sub_med = td.Medium(permittivity=1.46**2)\n", "\n", "# structures\n", "box1 = td.Structure(\n", " geometry=td.Box(\n", " center=(0, -box_length / 2 - slot_width / 2, 0),\n", " size=(td.inf, box_length, box_height)),\n", " medium=si_med\n", ")\n", "box2 = td.Structure(\n", " geometry=td.Box(\n", " center=(0, box_length / 2 + slot_width / 2, 0),\n", " size=(td.inf, box_length, box_height)),\n", " medium=si_med\n", ")\n", "\n", "# boundaries\n", "boundary_spec = td.BoundarySpec.all_sides(boundary=td.PML())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Define a helper function to show us the various grids as we go along this exmaple." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "execution": { "iopub.execute_input": "2022-07-21T20:54:15.150854Z", "iopub.status.busy": "2022-07-21T20:54:15.150728Z", "iopub.status.idle": "2022-07-21T20:54:15.153632Z", "shell.execute_reply": "2022-07-21T20:54:15.153335Z" } }, "outputs": [], "source": [ "# Plot simulation and overlay grid in the yz and xy planes\n", "def plot_sim_grid(sim):\n", " fig, ax = plt.subplots(1, 2, figsize=(12, 6))\n", " sim.plot(x=0, ax=ax[0])\n", " sim.plot_grid(x=0, ax=ax[0], lw=0.4, colors='r')\n", " ax[0].set_xlim(-0.6, 0.6)\n", " ax[0].set_ylim(-0.4, 0.4)\n", " sim.plot(z=0, ax=ax[1])\n", " sim.plot_grid(z=0, ax=ax[1], lw=0.4, colors='r')\n", " ax[1].set_xlim(-0.6, 0.6)\n", " ax[1].set_ylim(-0.4, 0.4)\n", " print(f\"Total number of grid points (millions): {sim.num_cells / 1e6:1.2}\")\n", " \n", " return ax" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Uniform grid\n", "\n", "The most standard way to define a simulation is to use a constant grid size in each of the three directions. This can be achieved simply using `td.GridSpec.uniform(dl=...)` as shown below." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "execution": { "iopub.execute_input": "2022-07-21T20:54:15.154844Z", "iopub.status.busy": "2022-07-21T20:54:15.154713Z", "iopub.status.idle": "2022-07-21T20:54:15.315487Z", "shell.execute_reply": "2022-07-21T20:54:15.315061Z" } }, "outputs": [ { "data": { "text/html": [ "
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}, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sim_uniform = td.Simulation(\n", " size=[5, 3, 3],\n", " grid_spec=td.GridSpec.uniform(dl=20*nm),\n", " medium=sub_med,\n", " structures=[box1, box2],\n", " boundary_spec=boundary_spec,\n", " run_time=1e-12\n", ")\n", "\n", "plot_sim_grid(sim_uniform);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Automatic nonuniform grid\n", "\n", "By default, Tidy3D uses a more advanced meshing, using an algorithm to automatically define a nonuniform grid in all three directions. The resolution of this grid is specified using the desired minimum steps per wavelength in each material (`min_steps_per_wvl = 10` by default). This specification therefore requires a target wavelength, which can be either provided directly to `grid_spec`, or inferred from any sources present in the simulation. The two simulations below will have the same grid." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "execution": { "iopub.execute_input": "2022-07-21T20:54:15.317061Z", "iopub.status.busy": "2022-07-21T20:54:15.316896Z", "iopub.status.idle": "2022-07-21T20:54:15.536671Z", "shell.execute_reply": "2022-07-21T20:54:15.536290Z" } }, "outputs": [ { "data": { "text/html": [ "
           WARNING  No sources in simulation.                               simulation.py:406\n",
       "
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           INFO     Auto meshing using wavelength 1.5500 defined from        grid_spec.py:472\n",
       "                    sources.                                                                 \n",
       "
\n" ], "text/plain": [ "\u001b[2;36m \u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO \u001b[0m Auto meshing using wavelength \u001b[1;36m1.5500\u001b[0m defined from \u001b]8;id=851320;file:///home/shashwat/flexcompute/repositories/tidy3d-docs/tidy3d/tidy3d/components/grid/grid_spec.py\u001b\\\u001b[2mgrid_spec.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=832159;file:///home/shashwat/flexcompute/repositories/tidy3d-docs/tidy3d/tidy3d/components/grid/grid_spec.py#472\u001b\\\u001b[2m472\u001b[0m\u001b]8;;\u001b\\\n", "\u001b[2;36m \u001b[0m sources. \u001b[2m \u001b[0m\n" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Total number of grid points (millions): 0.53\n" ] }, { "data": { "text/html": [ "
<Figure size 864x432 with 2 Axes>\n",
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}, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# provide wavelength directly\n", "sim_nonuniform_10 = td.Simulation(\n", " size=[5, 3, 3],\n", " grid_spec=td.GridSpec.auto(wavelength=wavelength),\n", " medium=sub_med,\n", " structures=[box1, box2],\n", " boundary_spec=boundary_spec,\n", " run_time=1e-12\n", ")\n", "\n", "# no grid_spec defined; using default with wavelength taken from source\n", "source = td.ModeSource(\n", " center=(-2, 0, 0),\n", " size=(0, 2, 2),\n", " mode_index=0,\n", " direction=\"+\",\n", " source_time=td.GaussianPulse(freq0=freq0, fwidth=freq0 / 10)\n", ")\n", "sim_nonuniform_10 = td.Simulation(\n", " size=[5, 3, 3],\n", " medium=sub_med,\n", " structures=[box1, box2],\n", " boundary_spec=boundary_spec,\n", " run_time=1e-12,\n", " sources=[source],\n", ")\n", "\n", "ax = plot_sim_grid(sim_nonuniform_10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The default grid is somewhat coarse, but it is often good for initial simulations. We can easily refine it by requiring a higher number of steps per wavelength:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "execution": { "iopub.execute_input": "2022-07-21T20:54:15.538269Z", "iopub.status.busy": "2022-07-21T20:54:15.538140Z", "iopub.status.idle": "2022-07-21T20:54:15.690084Z", "shell.execute_reply": "2022-07-21T20:54:15.689702Z" } }, "outputs": [ { "data": { "text/html": [ "
           WARNING  No sources in simulation.                               simulation.py:406\n",
       "
\n" ], "text/plain": [ "\u001b[2;36m \u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING \u001b[0m No sources in simulation. \u001b]8;id=597902;file:///home/shashwat/flexcompute/repositories/tidy3d-docs/tidy3d/tidy3d/components/simulation.py\u001b\\\u001b[2msimulation.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=760853;file:///home/shashwat/flexcompute/repositories/tidy3d-docs/tidy3d/tidy3d/components/simulation.py#406\u001b\\\u001b[2m406\u001b[0m\u001b]8;;\u001b\\\n" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Total number of grid points (millions): 2.3\n" ] }, { "data": { "text/html": [ "
<Figure size 864x432 with 2 Axes>\n",
       "
\n" ], "text/plain": [ "\u001b[1m<\u001b[0m\u001b[1;95mFigure\u001b[0m\u001b[39m size 864x432 with \u001b[0m\u001b[1;36m2\u001b[0m\u001b[39m Axes\u001b[0m\u001b[1m>\u001b[0m\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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}, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sim_nonuniform_20 = td.Simulation(\n", " size=[5, 3, 3],\n", " grid_spec=td.GridSpec.auto(\n", " wavelength=wavelength,\n", " min_steps_per_wvl=20,\n", " ),\n", " medium=sub_med,\n", " structures=[box1, box2],\n", " boundary_spec=boundary_spec,\n", " run_time=1e-12\n", ")\n", "ax = plot_sim_grid(sim_nonuniform_20)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Notice how the automatic grid conforms to the structures in the simulation, and the grid steps are high in the high-index regions. For this particular problem, we may want to do a final refinement around the slot, where we expect the fields to be strongest. This can be achieved by adding `override_structures` to the simulation `grid_spec`. This is a list of Tidy3D structures each with an arbitrary geometry and medium which are used exclusively for the meshing, added on top of any physical simulation structures." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "execution": { "iopub.execute_input": "2022-07-21T20:54:15.691706Z", "iopub.status.busy": "2022-07-21T20:54:15.691578Z", "iopub.status.idle": "2022-07-21T20:54:15.856671Z", "shell.execute_reply": "2022-07-21T20:54:15.856290Z" } }, "outputs": [ { "data": { "text/html": [ "
           WARNING  No sources in simulation.                               simulation.py:406\n",
       "
\n" ], "text/plain": [ "\u001b[2;36m \u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING \u001b[0m No sources in simulation. \u001b]8;id=174327;file:///home/shashwat/flexcompute/repositories/tidy3d-docs/tidy3d/tidy3d/components/simulation.py\u001b\\\u001b[2msimulation.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=734257;file:///home/shashwat/flexcompute/repositories/tidy3d-docs/tidy3d/tidy3d/components/simulation.py#406\u001b\\\u001b[2m406\u001b[0m\u001b]8;;\u001b\\\n" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Total number of grid points (millions): 4.0\n" ] }, { "data": { "text/html": [ "
<Figure size 864x432 with 2 Axes>\n",
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}, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Define a \"dummy\" box with refractive index 5 around the central location of the waveguide\n", "refine_box = td.Structure(\n", " geometry=td.Box(center=(0, 0, 0), size=(td.inf, 0.4, 0.4)),\n", " medium=td.Medium(permittivity=5 ** 2),\n", ")\n", "\n", "# Use the box as a grid refinement structure\n", "sim_nonuniform_20_refine = td.Simulation(\n", " size=[5, 3, 3],\n", " grid_spec=td.GridSpec.auto(\n", " wavelength=wavelength,\n", " min_steps_per_wvl=20,\n", " override_structures=[refine_box],\n", " ),\n", " medium=sub_med,\n", " structures=[box1, box2],\n", " boundary_spec=boundary_spec,\n", " run_time=1e-12\n", ")\n", "\n", "ax = plot_sim_grid(sim_nonuniform_20_refine)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that the override region is highlighted in the grid plotting. Now this looks pretty good - the grid size of the points around the slot region is `1550 / 20 / 5 = 15.5` nanometers, while overall the simulation has twice fewer total number of grid points (`4.1M`) compared to the simulation with a uniform mesh step of `20` nanometers (`8.3M`). Just for fun, we can also have a look at the modes of this structure." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "execution": { "iopub.execute_input": "2022-07-21T20:54:15.858333Z", "iopub.status.busy": "2022-07-21T20:54:15.858207Z", "iopub.status.idle": "2022-07-21T20:54:16.921449Z", "shell.execute_reply": "2022-07-21T20:54:16.921016Z" } }, "outputs": [ { "data": { "text/html": [ "
<Figure size 864x1008 with 12 Axes>\n",
       "
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}, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "num_modes = 3\n", "plane = td.Box(center=(0, 0, 0), size=(0, 2, 2))\n", "mode_spec=td.ModeSpec(num_modes=num_modes)\n", "ms = ModeSolver(simulation=sim_nonuniform_20_refine, plane=plane, mode_spec=mode_spec, freqs=[freq0])\n", "modes = ms.solve()\n", "fig, axs = plt.subplots(num_modes, 2, figsize=(12, 14))\n", "for mode_ind in range(num_modes):\n", " modes.plot_field(\"Ey\", \"abs\", freq=freq0, mode_index=mode_ind, ax=axs[mode_ind, 0], robust=False)\n", " modes.plot_field(\"Ez\", \"abs\", freq=freq0, mode_index=mode_ind, ax=axs[mode_ind, 1], robust=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Combination of uniform and nonuiform grid\n", "\n", "One last thing to note is that our refinement structure had the unexpected effect of making the mesh along the propagation direction very fine. In the most general case, we can define different grid specifications along each of the three simulation directions. For example, we can set a fixed grid size of `40nm` along the propagation direction, where we expect the field dependence to be smooth, while still using the refined nonuniform mesh in the other two directions." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "execution": { "iopub.execute_input": "2022-07-21T20:54:16.923466Z", "iopub.status.busy": "2022-07-21T20:54:16.923341Z", "iopub.status.idle": "2022-07-21T20:54:17.139652Z", "shell.execute_reply": "2022-07-21T20:54:17.139302Z" } }, "outputs": [ { "data": { "text/html": [ "
[16:54:16] WARNING  No sources in simulation.                               simulation.py:406\n",
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}, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "grid_spec = td.GridSpec(\n", " grid_x = td.UniformGrid(dl=0.04),\n", " grid_y = td.AutoGrid(min_steps_per_wvl=20),\n", " grid_z = td.AutoGrid(min_steps_per_wvl=20),\n", " wavelength=wavelength,\n", " override_structures=[refine_box]\n", ")\n", "\n", "sim_nonuniform_yz_uniform_x = td.Simulation(\n", " size=[5, 3, 3],\n", " grid_spec=grid_spec,\n", " medium=sub_med,\n", " structures=[box1, box2],\n", " boundary_spec=boundary_spec,\n", " run_time=1e-12\n", ")\n", "\n", "ax = plot_sim_grid(sim_nonuniform_yz_uniform_x)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The grid in the `yz`-plane is the same as in the previous plot, but the total number of grid points is more than twice fewer!" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.10" } }, "nbformat": 4, "nbformat_minor": 4 }