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+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "editable": true,
+ "slideshow": {
+ "slide_type": ""
+ },
+ "tags": []
+ },
+ "source": [
+ "# Adjoint Optimization of Rectangular Patch Antennas\n",
+ "\n",
+ "This notebook demonstrates the use of inverse design for the optimization of antennas. A simple rectangular patch antenna, as explored in our [Antenna Characteristics](https://www.flexcompute.com/tidy3d/examples/notebooks/AntennaCharacteristics/) notebook, is primarily defined by two parameters: its width and height. These are tuned to make the antenna resonate at a desired frequency and match the impedance of the feed line. However, to achieve wider bandwidth or specialized features like circular polarization, designers often move beyond simple rectangular shapes. For instance our [Circularly Polarized Patch Antenna](https://www.flexcompute.com/tidy3d/examples/notebooks/CircularlyPolarizedPatchAntenna/) notebook reproduces a non-rectangular radiator with parasitic strips to produce circularly polarized radiation.\n",
+ "\n",
+ "As antenna geometries become more intricate, the number of design parameters can increase dramatically, making traditional optimization methods inefficient. This notebook demonstrates a more powerful approach using gradient-based optimization in Tidy3D. This technique, enabled by the adjoint method and automatic differentiation (autograd), allows for the efficient and simultaneous optimization of all geometric parameters, also known as inverse design. We will illustrate this process with two examples:\n",
+ "1. A straightforward optimization to find the ideal width and height of a rectangular patch antenna for a single target frequency.\n",
+ "2. A more complex antenna for dual-band operation consisting of an optimizable inset, fins, and passive radiating elements. The optimizer simultaneously tunes the dimensions and positions of all structures to make the antenna resonant and matched at two design frequencies.\n",
+ "In both cases, we hold the substrate material and thickness constant, focusing exclusively on optimizing the antenna's shape.\n",
+ "\n",
+ "The two optimization geometries are shown below. Both antennas consist of a metallic patch on a substrate with a ground plane, are excited by an offset feed line, and radiate into free space. The first design is a simple rectangle defined by its width and height, while the second, more complex design involves optimizing the dimensions and relative positions of multiple structures at once.\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ "
"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "editable": true,
+ "slideshow": {
+ "slide_type": ""
+ },
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# Tidy3d import and set logging level\n",
+ "import tidy3d as td\n",
+ "\n",
+ "# External modules needed for this notebook\n",
+ "import numpy as np\n",
+ "import autograd.numpy as anp\n",
+ "from autograd import value_and_grad\n",
+ "import optax\n",
+ "import os\n",
+ "import pickle\n",
+ "\n",
+ "# Tidy3d plugin import\n",
+ "import tidy3d.plugins.smatrix as smatrix\n",
+ "from tidy3d.plugins.microwave import rf_material_library\n",
+ "from tidy3d.web import run\n",
+ "\n",
+ "# Libraries and configuration for printing and display\n",
+ "import matplotlib.pyplot as plt\n",
+ "import matplotlib.patches as patches\n",
+ "from rich.console import Console\n",
+ "from rich.text import Text\n",
+ "\n",
+ "# Setup console and printing parameters for rich printing during optimization loops\n",
+ "console = Console()\n",
+ "print_decimal_places = 2 # how many decimal places to use in printing\n",
+ "print_iteration_frequency = 5 # how often to print optimization progress"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Configuration and Parameters\n",
+ "\n",
+ "In this first section, we set up a variety of parameters for the optimization problems, including the frequency bands (specified in Hz) and resonance targets as well as useful geometric parameters. Similar to other RF examples, we introduce a scaling factor to convert the default unit in Tidy3D of micrometers to millimeters (mm), which is more commonly used in antenna simulations. Thus, the default unit when looking at constants in this notebook is millimeters."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# length scales and geometric parameters for optimization\n",
+ "mm = 1e3 # mm scaling\n",
+ "\n",
+ "# feedline parameters\n",
+ "feed_x = 2.46 * mm # width of feed\n",
+ "feed_y = 20 * mm # length of feed\n",
+ "feed_offset = 2.09 * mm # feed offset from center of patch\n",
+ "\n",
+ "# substrate geometric parameters - broken into substrate sizes for the single patch optimization that comes\n",
+ "# first and the multi-patch optimization that comes second where we need a larger substrate to\n",
+ "# support the larger antenna geometry\n",
+ "single_patch_sub_width = 23.34 * mm\n",
+ "single_patch_sub_height_extension = 25 * mm\n",
+ "\n",
+ "sub_x_single_patch = [-0.5 * single_patch_sub_width, 0.5 * single_patch_sub_width]\n",
+ "sub_y_single_patch = [-feed_y, single_patch_sub_height_extension]\n",
+ "sub_z = 0.68 * mm\n",
+ "\n",
+ "multi_patch_sub_width = 3 * 23.34 * mm\n",
+ "multi_patch_sub_height_extension = 1.75 * 25 * mm\n",
+ "\n",
+ "sub_x_multi_patch = [-0.5 * multi_patch_sub_width, 0.5 * multi_patch_sub_width]\n",
+ "sub_y_multi_patch = [-feed_y, multi_patch_sub_height_extension]\n",
+ "\n",
+ "# frequency range (Hz)\n",
+ "freq_start = 7e9\n",
+ "freq_stop = 11e9\n",
+ "freq_bounds = [freq_start, freq_stop]\n",
+ "# simulation frequencies to cover enough bandwidth for evaluating and optimizing antennas\n",
+ "opt_sim_freqs = [8e9, 10e9]\n",
+ "\n",
+ "freq0 = (freq_start + freq_stop) / 2 # central frequency\n",
+ "wavelength0 = td.C_0 / freq0 # wavelength of centeral frequency in vacuum\n",
+ "\n",
+ "freqs = np.linspace(freq_start, freq_stop, 200)\n",
+ "\n",
+ "# frequencies for computing S-parameters of antennas\n",
+ "freqs_s_params = freqs\n",
+ "\n",
+ "# frequencies for optimizing different\n",
+ "opt_freqs_single_patch = [8.25e9] # single patch target frequency\n",
+ "num_opt_freqs_multi_patch = 2\n",
+ "# optimize multi patch geometry for dual resonance\n",
+ "opt_freqs_multi_patch = np.linspace(8e9, 9e9, num_opt_freqs_multi_patch)\n",
+ "\n",
+ "# materials for optimization\n",
+ "air = td.Medium() # set up the antennas so they radiate into air\n",
+ "# choose common PCB material, ArlonAD255C, from the RF material library to use as substrate\n",
+ "sub_medium = rf_material_library[\"AD255C\"][\"design\"]\n",
+ "PEC = td.PEC2D # thickness-free PEC medium for antenna patches, feed lines, and the ground plane"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Create Simulation Objects\n",
+ "\n",
+ "To prepare for the optimization, we need to make a base simulation that we can add the antenna geometry and excitation source to. This base simulation includes the ground plane and substrate, the two structures below the patch antenna. In this simulation, we also set up a `MeshOverrideStructure` to cover the region of the simulation with PEC. We use a very fine mesh here to improve accuracy of both the simulation and PEC gradients. As in other RF examples, the source will be added later via a `LumpedPort`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def create_base_sim(sub_x_bounds, sub_y_bounds):\n",
+ " \"\"\"\n",
+ " Creates the base simulation for evaluating and optimizing patch antennas. The base simulation includes the\n",
+ " substrate geometry, and ground plane. It also overrides a vertical slice of the simulation where the feed and\n",
+ " patch will be placed with a fine mesh override to ensure accurate simulation and gradients near the PEC boundaries.\n",
+ " \"\"\"\n",
+ " substrate_box = td.Box.from_bounds(\n",
+ " [sub_x_bounds[0], sub_y_bounds[0], -sub_z / 2.0],\n",
+ " [sub_x_bounds[1], sub_y_bounds[1], sub_z / 2.0],\n",
+ " )\n",
+ " # Define substrate structure\n",
+ " substrate = td.Structure(\n",
+ " geometry=substrate_box,\n",
+ " medium=sub_medium,\n",
+ " )\n",
+ "\n",
+ " # Define ground plane structure and assign the material by PEC\n",
+ " ground_plane = td.Structure(\n",
+ " geometry=substrate_box.updated_copy(\n",
+ " center=list(substrate_box.center[0:2]) + [-sub_z / 2.0],\n",
+ " size=list(substrate_box.size[0:2]) + [0],\n",
+ " ),\n",
+ " medium=PEC,\n",
+ " )\n",
+ "\n",
+ " # list of structures for the simulation arranged first by dielectric and then PEC to\n",
+ " # ensure PEC takes precedence at interfaces.\n",
+ " structures_list = [substrate, ground_plane]\n",
+ "\n",
+ " # PML wavelength at 10 GHz\n",
+ " wl_pml = wavelength0\n",
+ "\n",
+ " # quarter wavelength (at 10 GHz) padding on each side\n",
+ " sim_x_size = sub_x_bounds[1] - sub_x_bounds[0] + wl_pml / 2.0\n",
+ " sim_y_size = sub_y_bounds[1] - sub_y_bounds[0] + wl_pml / 2.0\n",
+ " sim_y_center = np.mean(sub_y_bounds)\n",
+ "\n",
+ " sim_z_max = sub_z + 1.5 * wavelength0\n",
+ " sim_z_min = sub_z - 0.5 * wavelength0\n",
+ " sim_z_center = 0.5 * (sim_z_max + sim_z_min)\n",
+ " sim_z_size = sim_z_max - sim_z_min\n",
+ "\n",
+ " # set a fine mesh based on the center wavelength\n",
+ " dl = wavelength0 / 200.0\n",
+ " mesh_override_vertical_padding = 1 * mm\n",
+ "\n",
+ " mesh_overrides = [\n",
+ " td.MeshOverrideStructure(\n",
+ " geometry=td.Box(\n",
+ " center=(0, sim_y_center, 0.0),\n",
+ " size=(sim_x_size, sim_y_size, sub_z + mesh_override_vertical_padding),\n",
+ " ),\n",
+ " dl=[dl, dl, dl],\n",
+ " )\n",
+ " ]\n",
+ "\n",
+ " # Truncate the computational domain by PMLs\n",
+ " boundary_spec = td.BoundarySpec(\n",
+ " x=td.Boundary.pml(),\n",
+ " y=td.Boundary.pml(),\n",
+ " z=td.Boundary.pml(),\n",
+ " )\n",
+ "\n",
+ " # Create the simulation object\n",
+ " base_sim = td.Simulation(\n",
+ " center=[0.0, sim_y_center, sim_z_center],\n",
+ " size=[sim_x_size, sim_y_size, sim_z_size],\n",
+ " grid_spec=td.GridSpec.auto(\n",
+ " min_steps_per_wvl=20, # largest cell size is set to 20 cells per smallest wavelength.\n",
+ " wavelength=td.C_0 / freq_stop, # smallest wavelength to resolve\n",
+ " override_structures=mesh_overrides, # override the mesh around the antenna and feed for accuracy\n",
+ " ),\n",
+ " structures=structures_list,\n",
+ " sources=[], # sources will be added by TerminalComponentModeler\n",
+ " monitors=[],\n",
+ " run_time=70 * (substrate.geometry.size[1] / td.C_0),\n",
+ " boundary_spec=boundary_spec,\n",
+ " plot_length_units=\"mm\", # this option will make plots default to units of millimeters.\n",
+ " )\n",
+ "\n",
+ " return base_sim"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In addition to the base simulation creation, we set up a function to add a feed line and PEC patches to a simulation object. Later, this will allow us to create an antenna with an arbitrary list of patches and insert it into our base simulation."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def create_simulation_with_patches(base_sim, patch_boxes, monitors):\n",
+ " \"\"\"\n",
+ " Creates a new simulation from the base simulation that includes PEC patches for all of the Box\n",
+ " objects in patch_boxes as well as adds a feed line.\n",
+ " \"\"\"\n",
+ " patches = []\n",
+ " for patch_box in patch_boxes:\n",
+ " patches.append(td.Structure(geometry=patch_box, medium=PEC))\n",
+ "\n",
+ " feed_geometry = td.Box.from_bounds(\n",
+ " [feed_offset - 0.5 * feed_x, -feed_y, sub_z / 2], [feed_offset + 0.5 * feed_x, 0, sub_z / 2]\n",
+ " )\n",
+ "\n",
+ " feed = td.Structure(geometry=feed_geometry, medium=PEC)\n",
+ "\n",
+ " return base_sim.updated_copy(\n",
+ " structures=list(base_sim.structures) + [feed] + patches,\n",
+ " monitors=list(base_sim.monitors) + monitors,\n",
+ " )"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Finally, we set up a function to create a `LumpedPort` and `TerminalComponentModeler` that will create the input excitation to the antenna. We can specify an impedance for the port as well as the desired frequencies for the resulting simulation. The `TerminalComponentModeler` will set up the simulations we need to evaluate and optimize the antenna."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def create_modeler(simulation, frequencies, radiation_monitors=(), port_impedance=50):\n",
+ " \"\"\"\n",
+ " Creates the LumpedPort and TerminalComponentModeler based on a `simulation`, the desired\n",
+ " simulation `frequencies`, `radiation_monitors` for computing directivity, and a `port_impedance`.\n",
+ " \"\"\"\n",
+ " # Setup a LumpedPort for the TerminalComponentModeler, which is needed\n",
+ " # to end the port with a matched load as well as providing a source for the simulation.\n",
+ " port = smatrix.LumpedPort(\n",
+ " center=[feed_offset, -feed_y, 0],\n",
+ " size=[feed_x, 0, sub_z],\n",
+ " voltage_axis=2,\n",
+ " name=\"lumped_port\",\n",
+ " impedance=port_impedance,\n",
+ " )\n",
+ "\n",
+ " # We integrate the base simulation with the LumpedPort using the TerminalComponentModeler.\n",
+ " # This allows us to compute scattering parameters and extract any additional data needed from the simulation.\n",
+ " modeler = smatrix.TerminalComponentModeler(\n",
+ " simulation=simulation,\n",
+ " ports=[port],\n",
+ " freqs=frequencies,\n",
+ " remove_dc_component=False, # include DC component for more accuracy at low frequencies\n",
+ " radiation_monitors=radiation_monitors,\n",
+ " )\n",
+ "\n",
+ " return modeler"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Setup Plotting and Evaluation Functions\n",
+ "\n",
+ "Here, we set up some helper functions to aid in plotting antenna geometry and evaluating antenna characteristics before and after optimizations. First, we set up a function to plot the antenna structure and feed line along with the surrounding mesh. Near PEC edges, especially when computing gradients, we recommend using a fine mesh. In this function, we can also observe the location where the input source is fed into the antenna to confirm it is at the end of the feed line."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def plot_structures_and_mesh(patch_boxes, sub_x_bounds, sub_y_bounds):\n",
+ " \"\"\"Plots the antenna structure and surrounding mesh to ensure it looks as expected before running simulations.\"\"\"\n",
+ " base_sim = create_base_sim(sub_x_bounds, sub_y_bounds)\n",
+ "\n",
+ " no_additional_monitors = []\n",
+ " sim_with_patches = create_simulation_with_patches(base_sim, patch_boxes, no_additional_monitors)\n",
+ " mesh_modeler = create_modeler(sim_with_patches, freqs)\n",
+ "\n",
+ " sim_temp = list(mesh_modeler.sim_dict.values())[0]\n",
+ "\n",
+ " sim_x_bounds = [\n",
+ " sim_temp.center[0] - 0.5 * sim_temp.size[0],\n",
+ " sim_temp.center[0] + 0.5 * sim_temp.size[0],\n",
+ " ]\n",
+ "\n",
+ " sim_y_bounds = [\n",
+ " sim_temp.center[1] - 0.5 * sim_temp.size[1],\n",
+ " sim_temp.center[1] + 0.5 * sim_temp.size[1],\n",
+ " ]\n",
+ "\n",
+ " fig, ax = plt.subplots()\n",
+ "\n",
+ " # examine the structure and mesh in the x-y plane\n",
+ " sim_temp.plot(\n",
+ " z=sub_z / 2,\n",
+ " ax=ax,\n",
+ " hlim=sim_x_bounds,\n",
+ " vlim=sim_y_bounds,\n",
+ " monitor_alpha=0.2,\n",
+ " )\n",
+ " sim_temp.plot_grid(z=sub_z / 2, ax=ax, hlim=sim_x_bounds, vlim=sim_y_bounds)\n",
+ "\n",
+ " plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Second, we set up a function that can calculate S11 and realized antenna gain for each polarization. S11 is the voltage reflection coefficient and $|S11|^2$ is the power reflection coefficient or in other words, the reflected power divided by the input power. A good antenna will have a small S11 at its operating frequencies. S11 is plotted in dB and thus at resonance, we will see a large, negative value corresponding to low reflection. Before and after an optimization, this is one way to evaluate how well the optimization tuned the geometry for the correct frequencies. In the realized gain plot, we can see how efficiently energy is radiating from the antenna and in what direction when compared to an isotropic radiator. For simplicity and to keep plots less crowded, we collect the realized gain for the optimization frequencies while we compute S11 value over a broad spectrum."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def evaluate_antenna(patch_boxes, eval_s_params_freqs, opt_freqs, sub_x_bounds, sub_y_bounds):\n",
+ " \"\"\"\n",
+ " Evaluate the S11 parameter and realized gain in both polarizations for the given antenna geometry.\n",
+ " The S11 is calculated across a broad spectrum while the realized gain is only computed for the opt_freqs.\n",
+ " \"\"\"\n",
+ " base_sim = create_base_sim(sub_x_bounds, sub_y_bounds)\n",
+ " no_additional_monitors = []\n",
+ " sim_with_patches = create_simulation_with_patches(base_sim, patch_boxes, no_additional_monitors)\n",
+ "\n",
+ " theta = np.linspace(-np.pi, np.pi, 200)\n",
+ " phi = np.linspace(0, np.pi, 100)\n",
+ "\n",
+ " directivity_center_x = np.mean(sub_x_bounds)\n",
+ " directivity_center_y = np.mean(sub_y_bounds)\n",
+ " directivity_size_x = 5 * mm + sub_x_bounds[1] - sub_x_bounds[0]\n",
+ " directivity_size_y = 5 * mm + sub_y_bounds[1] - sub_y_bounds[0]\n",
+ "\n",
+ " monitor_directivity = td.DirectivityMonitor(\n",
+ " center=[directivity_center_x, directivity_center_y, 0],\n",
+ " size=(\n",
+ " directivity_size_x,\n",
+ " directivity_size_y,\n",
+ " 4 * mm,\n",
+ " ),\n",
+ " freqs=opt_freqs,\n",
+ " name=\"directivity\",\n",
+ " phi=list(phi),\n",
+ " theta=list(theta),\n",
+ " far_field_approx=True,\n",
+ " )\n",
+ "\n",
+ " sim_with_directivity = sim_with_patches\n",
+ " # we only need whatever frequencies are unique in these two lists to have all the data we need for computing\n",
+ " # S11 and gain\n",
+ " eval_freqs = np.unique(list(eval_s_params_freqs) + list(opt_freqs))\n",
+ "\n",
+ " modeler = create_modeler(\n",
+ " sim_with_directivity, eval_freqs, radiation_monitors=[monitor_directivity]\n",
+ " )\n",
+ " smatrix_data = run(modeler, task_name=\"smatrix\", verbose=False)\n",
+ "\n",
+ " antenna_parameters_freq = smatrix_data.get_antenna_metrics_data(monitor_name=\"directivity\")\n",
+ " partial_realized_gain = antenna_parameters_freq.partial_realized_gain(pol_basis=\"linear\")\n",
+ "\n",
+ " return smatrix_data, theta, partial_realized_gain"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Given the optimization results, we can plot the comparison of two antenna simulations using the `plot_antenna_comparison` function below. We can easily compare the initial and final optimization states to see how well the resulting antenna is performing in our desired metrics."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def plot_antenna_comparison(\n",
+ " s_matrix_list,\n",
+ " sim_data_list,\n",
+ " partial_realized_gain_list,\n",
+ " opt_freqs,\n",
+ " theta,\n",
+ " plot_phi,\n",
+ " plot_title=\"Antenna Simulation Comparison\",\n",
+ " sim_names=None,\n",
+ " single_color_gain_plots=False,\n",
+ " savefig_fname=None,\n",
+ "):\n",
+ " \"\"\"\n",
+ " Plots a comparison of two sets of simulation data.\n",
+ "\n",
+ " Args:\n",
+ " s_matrix_list: List of S-matrix objects [s_matrix_sim1, s_matrix_sim2].\n",
+ " sim_data_list: List of sim_data objects [sim_data_sim1, sim_data_sim2],\n",
+ " where each sim_data contains a \"radiation\" DirectivityMonitor output.\n",
+ " partial_realized_gain_list: List of realized gain in linear polarization for each simulation.\n",
+ " opt_freqs: List of optimization frequencies to plot for directivity.\n",
+ " theta: Numpy array of theta angles for polar plots.\n",
+ " plot_phi: The phi value to select in the realized gain data for plotting.\n",
+ " plot_title: Optional title to specify for plot (default is \"Antenna Simulation Comparison\").\n",
+ " sim_names: Optional list of names for simulations for clearer legends.\n",
+ " single_color_gain_plots: Optional choice to use a single color for all the frequencies\n",
+ " in the gain plot (True) or have each frequency a different\n",
+ " color (False). Default is False.\n",
+ " savefig_fname: An optional filename to save the resulting figure\n",
+ " \"\"\"\n",
+ "\n",
+ " num_simulations = len(s_matrix_list)\n",
+ " if num_simulations != 2 or len(sim_data_list) != 2:\n",
+ " print(\"Warning: This function is designed to compare exactly two simulations.\")\n",
+ "\n",
+ " alphas = [0.5, 1.0] # Alpha for sim1, sim2\n",
+ " if sim_names is None or len(sim_names) != num_simulations:\n",
+ " sim_names = [f\"Sim {i + 1}\" for i in range(num_simulations)]\n",
+ "\n",
+ " grid_spec_cols = 3 # S11 and realized gain for each polarization side-by-side\n",
+ " fig_width = grid_spec_cols * 4.5\n",
+ " fig_height = 6.5\n",
+ "\n",
+ " num_rows = 1\n",
+ "\n",
+ " fig = plt.figure(figsize=(fig_width, fig_height), constrained_layout=True)\n",
+ " gs = fig.add_gridspec(num_rows, grid_spec_cols)\n",
+ " axs_list = []\n",
+ "\n",
+ " # plot the S11 parameter comparison for each simulation\n",
+ " ax_s11 = fig.add_subplot(gs[0, 0])\n",
+ " axs_list.append(ax_s11)\n",
+ " ax_s11.set_title(\"$S_{11}$ Coefficient\")\n",
+ " ax_s11.set_xlabel(\"Frequency (GHz)\")\n",
+ " ax_s11.set_ylabel(\"$|S_{11}|^2$ (dB)\")\n",
+ "\n",
+ " for sim_idx in range(num_simulations):\n",
+ " s_matrix = s_matrix_list[sim_idx]\n",
+ " current_alpha = alphas[sim_idx]\n",
+ " sim_label_name = sim_names[sim_idx]\n",
+ "\n",
+ " s11_freqs_ghz = s_matrix.data.coords[\"f\"] / 1e9\n",
+ " s11_data_selection = s_matrix.data.isel(port_out=0, port_in=0)\n",
+ "\n",
+ " s11_values_flat = s11_data_selection.values.flatten()\n",
+ " s11_values_db = 20 * np.log10(np.abs(s11_values_flat))\n",
+ " ax_s11.plot(\n",
+ " s11_freqs_ghz,\n",
+ " s11_values_db,\n",
+ " alpha=current_alpha,\n",
+ " label=f\"{sim_label_name})\",\n",
+ " )\n",
+ "\n",
+ " impedances_norm = (1 + s11_values_flat) / (1 - s11_values_flat)\n",
+ "\n",
+ " for opt_freq in opt_freqs:\n",
+ " ax_s11.axvline(x=opt_freq / 1e9, color=\"k\", linestyle=\"--\")\n",
+ "\n",
+ " ax_s11.set_ylim(-25, 2)\n",
+ " ax_s11.grid(True)\n",
+ " ax_s11.legend()\n",
+ "\n",
+ " # for each linear polarization component, plot the realized gain for each linear polarization\n",
+ " polarization_components = [\"Gtheta\", \"Gphi\"]\n",
+ "\n",
+ " for pol_idx, pol_component in enumerate(polarization_components):\n",
+ " ax_polar = fig.add_subplot(gs[0, 1 + pol_idx], projection=\"polar\")\n",
+ " axs_list.append(ax_polar)\n",
+ " ax_polar.set_title(f\"Realized Gain for {pol_component}\")\n",
+ "\n",
+ " ax_polar.set_theta_direction(-1)\n",
+ " ax_polar.set_theta_offset(np.pi / 2.0)\n",
+ " ax_polar.grid(True)\n",
+ " ax_polar.set_rlabel_position(22.5)\n",
+ "\n",
+ " overall_max_gain = -np.inf\n",
+ " color_cycle = plt.rcParams[\"axes.prop_cycle\"].by_key()[\"color\"]\n",
+ "\n",
+ " for freq_idx, eval_freq in enumerate(opt_freqs):\n",
+ " base_color = color_cycle[freq_idx % len(color_cycle)]\n",
+ " if single_color_gain_plots:\n",
+ " base_color = color_cycle[0]\n",
+ " freq_label_for_legend = f\"{eval_freq / 1e9:.2f} GHz\"\n",
+ "\n",
+ " # Plot data for each simulation for the current frequency\n",
+ " for sim_idx in range(num_simulations):\n",
+ " sim_data = sim_data_list[sim_idx]\n",
+ " current_alpha = alphas[sim_idx]\n",
+ "\n",
+ " if single_color_gain_plots:\n",
+ " base_color = color_cycle[sim_idx]\n",
+ "\n",
+ " realized_gain_data = partial_realized_gain_list[sim_idx][pol_component].sel(\n",
+ " f=eval_freq, phi=plot_phi, method=\"nearest\"\n",
+ " )\n",
+ "\n",
+ " gain_values_for_plot = realized_gain_data.squeeze().values\n",
+ "\n",
+ " current_max_val = np.max(gain_values_for_plot)\n",
+ " if current_max_val > overall_max_gain:\n",
+ " overall_max_gain = current_max_val\n",
+ "\n",
+ " # Label only the second simulation's line (alpha=1.0) for the legend entry of this frequency\n",
+ " label_to_use = f\"{freq_label_for_legend} ({sim_names[sim_idx]})\"\n",
+ " ax_polar.plot(\n",
+ " theta,\n",
+ " gain_values_for_plot,\n",
+ " color=base_color,\n",
+ " alpha=current_alpha,\n",
+ " label=label_to_use,\n",
+ " )\n",
+ "\n",
+ " ax_polar.set_rlim(0, overall_max_gain * 1.1 if overall_max_gain > 0 else 1.0)\n",
+ " ax_polar.legend(title=\"Frequency (GHz)\", loc=\"best\", fontsize=\"small\")\n",
+ "\n",
+ " fig.suptitle(plot_title, fontsize=16)\n",
+ "\n",
+ " if savefig_fname:\n",
+ " plt.savefig(savefig_fname)\n",
+ "\n",
+ " plt.show()\n",
+ "\n",
+ "\n",
+ "def plot_antenna_evolution(s11_sq_dB, s11_f, partial_realized_gain, gain_freqs, theta):\n",
+ " \"\"\"Plots the evolution of the antenna S11 and and realized gain throughout an optimization.\n",
+ " Args:\n",
+ " s11_sq_dB: List of |S11|^2 (in dB) at each point in optimization. The length should match the\n",
+ " length of `partial_realized_gain`\n",
+ " s11_f: Frequencies for each S11 array\n",
+ " partial_realized_gain: List of realized gain objects broken into linear polarization. The length\n",
+ " of this list should match the length of `s11_sq_dB`\n",
+ " gain_freqs: List of frequencies for each realized gain object.\n",
+ " theta: List of theta values for each realized gain object.\n",
+ " \"\"\"\n",
+ " num_lines = len(s11_sq_dB)\n",
+ "\n",
+ " alphas = np.linspace(0.25, 1.0, num_lines)\n",
+ "\n",
+ " fig_width = 13.5\n",
+ " fig_height = 6.5\n",
+ "\n",
+ " fig = plt.figure(figsize=(fig_width, fig_height), constrained_layout=True)\n",
+ " num_rows = 1\n",
+ " grid_spec_cols = 3\n",
+ " gs = fig.add_gridspec(num_rows, grid_spec_cols)\n",
+ " axs_list = []\n",
+ "\n",
+ " ax_s11 = fig.add_subplot(gs[0, 0])\n",
+ " axs_list.append(ax_s11)\n",
+ " ax_s11.set_title(\"$S_{11}$ Coefficient\")\n",
+ " ax_s11.set_xlabel(\"Frequency (GHz)\")\n",
+ " ax_s11.set_ylabel(\"$|S_{11}|^2$ (dB)\")\n",
+ "\n",
+ " color_cycle = plt.rcParams[\"axes.prop_cycle\"].by_key()[\"color\"]\n",
+ "\n",
+ " for idx in range(0, num_lines):\n",
+ " ax_s11.plot(s11_f, s11_sq_dB[idx], color=color_cycle[0], linewidth=1.5, alpha=alphas[idx])\n",
+ "\n",
+ " ax_s11.set_ylim(-25, 2)\n",
+ " ax_s11.grid(True)\n",
+ "\n",
+ " def add_polar_sequence(grid_col, pol_component, title):\n",
+ " ax_polar = fig.add_subplot(gs[0, grid_col], projection=\"polar\")\n",
+ " axs_list.append(ax_polar)\n",
+ " ax_polar.set_title(f\"Realized Gain for {pol_component}\")\n",
+ "\n",
+ " ax_polar.set_theta_direction(-1)\n",
+ " ax_polar.set_theta_offset(np.pi / 2.0)\n",
+ " ax_polar.grid(True)\n",
+ " ax_polar.set_rlabel_position(22.5)\n",
+ "\n",
+ " for idx in range(0, num_lines):\n",
+ " partial_realized_gain_batch = partial_realized_gain[idx][pol_component]\n",
+ " partial_realized_gain_batch = np.reshape(\n",
+ " partial_realized_gain_batch, (len(gain_freqs), len(theta))\n",
+ " )\n",
+ "\n",
+ " for freq_idx in range(0, len(gain_freqs)):\n",
+ " gain_values_for_plot = partial_realized_gain_batch[freq_idx]\n",
+ "\n",
+ " ax_polar.plot(\n",
+ " theta, gain_values_for_plot, color=color_cycle[freq_idx], alpha=alphas[idx]\n",
+ " )\n",
+ "\n",
+ " ax_polar.set_title(title)\n",
+ "\n",
+ " add_polar_sequence(1, \"Gtheta\", f\"Realized Gain\\n(Gtheta), phi=0\")\n",
+ " add_polar_sequence(2, \"Gphi\", f\"Realized Gain\\n(Gphi), phi=0\")\n",
+ "\n",
+ " plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We set up a function to plot the figure of merit trajectory of the optimization and compare the initial and final antenna geometries. This is an indication of how well the optimization worked as well as a demonstration of the overall change that occurred over the course of the optimization. For the single patch antenna, these changes are small and show a tuning of the width and height, but we will see in the higher-dimensional optimization that the central and surrounding patches change significantly while optimizing for a dual band figure of merit."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def plot_optimization_results(\n",
+ " figure_of_merit, feed_rect, init_rectangles, final_rectangles, savefig_fname=None\n",
+ "):\n",
+ " \"\"\"\n",
+ " Plots a main rectangle and two lists of other rectangles with specified styles.\n",
+ "\n",
+ " Args:\n",
+ " figure_of_merit: Figure of merit for each iteration during the optimization.\n",
+ " feed_rect: The rectangular corresponding to the feed line.\n",
+ " init_rectangles: A list of rectangles corresponding to the initial state\n",
+ " of the optimization.\n",
+ " final_rectangles: A list of rectangles corresponding to the final condition\n",
+ " of the optimization.\n",
+ " savefig_fname: Optional filename to save the resulting figure.\n",
+ "\n",
+ " \"\"\"\n",
+ " fig, ax = plt.subplots(1, 2)\n",
+ " ax[1].set_aspect(\"equal\", adjustable=\"box\")\n",
+ "\n",
+ " all_rects_params = [] # to store (x_bl, y_bl, width, height) for limit calculation\n",
+ "\n",
+ " # helper function to add a rectangle and collect its parameters\n",
+ " def add_rectangle_to_plot(\n",
+ " rect_obj, facecolor, alpha=1.0, edgecolor=\"black\", linestyle=\"solid\", legend=None\n",
+ " ):\n",
+ " \"\"\"\n",
+ " Adds a single rectangle to the plot and collects its parameters.\n",
+ "\n",
+ " Args:\n",
+ " rect_obj: The rectangle object with 'center' and 'size'.\n",
+ " facecolor: The face color of the rectangle.\n",
+ " alpha: Optional transparency of the rectangle.\n",
+ " edgecolor: Optional edge color of the rectangle.\n",
+ " linestyle: Optional line style of the rectangle's border.\n",
+ " legend: Optional legend entry to use for this rectangle.\n",
+ " \"\"\"\n",
+ " center_x, center_y, _ = rect_obj.center\n",
+ " width, height, _ = rect_obj.size\n",
+ "\n",
+ " center_x /= mm\n",
+ " center_y /= mm\n",
+ " width /= mm\n",
+ " height /= mm\n",
+ "\n",
+ " # Calculate bottom-left corner coordinates\n",
+ " bottom_left_x = center_x - width / 2\n",
+ " bottom_left_y = center_y - height / 2\n",
+ "\n",
+ " all_rects_params.append((bottom_left_x, bottom_left_y, width, height))\n",
+ "\n",
+ " rect_patch = patches.Rectangle(\n",
+ " (bottom_left_x, bottom_left_y),\n",
+ " width,\n",
+ " height,\n",
+ " facecolor=facecolor,\n",
+ " alpha=alpha,\n",
+ " edgecolor=edgecolor,\n",
+ " linestyle=linestyle,\n",
+ " linewidth=1, # Default linewidth for borders\n",
+ " label=legend,\n",
+ " )\n",
+ " ax[1].add_patch(rect_patch)\n",
+ "\n",
+ " add_rectangle_to_plot(feed_rect, facecolor=\"gold\", edgecolor=\"black\")\n",
+ "\n",
+ " legends_init_rect = [\n",
+ " \"initial\" if (idx == 0) else None for idx in range(0, len(init_rectangles))\n",
+ " ]\n",
+ " legends_final_rect = [\n",
+ " \"final\" if (idx == 0) else None for idx in range(0, len(final_rectangles))\n",
+ " ]\n",
+ "\n",
+ " # plot rectangles from the second list (gold)\n",
+ " for idx, rect_obj in enumerate(final_rectangles):\n",
+ " add_rectangle_to_plot(\n",
+ " rect_obj, facecolor=\"gold\", edgecolor=\"black\", legend=legends_final_rect[idx]\n",
+ " ) # Added black edge for consistency\n",
+ "\n",
+ " # plot rectangles from the first list (gray, 0.25 alpha, dotted black border)\n",
+ " for idx, rect_obj in enumerate(init_rectangles):\n",
+ " add_rectangle_to_plot(\n",
+ " rect_obj,\n",
+ " facecolor=\"gray\",\n",
+ " alpha=0.25,\n",
+ " edgecolor=\"black\",\n",
+ " linestyle=\"dotted\",\n",
+ " legend=legends_init_rect[idx],\n",
+ " )\n",
+ "\n",
+ " # calculate plot limits\n",
+ " if all_rects_params:\n",
+ " min_x = min(p[0] for p in all_rects_params)\n",
+ " min_y = min(p[1] for p in all_rects_params)\n",
+ " max_x = max(p[0] + p[2] for p in all_rects_params) # max x is bottom_left_x + width\n",
+ " max_y = max(p[1] + p[3] for p in all_rects_params) # max y is bottom_left_y + height\n",
+ "\n",
+ " # add some padding to the limits\n",
+ " padding_x = (max_x - min_x) * 0.1 if (max_x - min_x) > 0 else 1\n",
+ " padding_y = (max_y - min_y) * 0.1 if (max_y - min_y) > 0 else 1\n",
+ "\n",
+ " ax[1].set_xlim(min_x - padding_x, max_x + padding_x)\n",
+ " ax[1].set_ylim(min_y - padding_y, max_y + padding_y)\n",
+ " else:\n",
+ " # default limits if no rectangles are plotted\n",
+ " ax[1].set_xlim(-5, 5)\n",
+ " ax[1].set_ylim(-5, 5)\n",
+ "\n",
+ " ax[1].set_title(\"Antenna Geometry\")\n",
+ " ax[1].set_xlabel(\"X-coordinate (mm)\")\n",
+ " ax[1].set_ylabel(\"Y-coordinate (mm)\")\n",
+ " ax[1].grid(True, linestyle=\"--\", alpha=0.7)\n",
+ "\n",
+ " ax[1].legend(loc=\"lower left\", bbox_to_anchor=(1.0, 0.75))\n",
+ "\n",
+ " ax[0].plot(figure_of_merit, color=\"green\", linewidth=2)\n",
+ " ax[0].set_title(\"Optimization\")\n",
+ " ax[0].set_xlabel(\"Iteration\")\n",
+ " ax[0].set_ylabel(\"Figure of Merit\")\n",
+ "\n",
+ " if savefig_fname:\n",
+ " plt.savefig(savefig_fname)\n",
+ "\n",
+ " plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Demonstrating Gradient Based Optimization of a Single Patch Antenna\n",
+ "\n",
+ "In our first optimization, we use the adjoint gradients to tune the width and height of a single patch antenna element. This is a good way to validate the gradients and optimization figures of merit are working as expected before moving onto a more complex optimization. As a first step, we create an objective function that we can use for both optimizations. This function takes in parameters defining the antenna geometry as well as a function to convert those parameters to `Box` geometries that can be imported into the simulation. Further, it allows us to specify the optimization monitors we want to use, which may be different depending on the overall optimization space. After adding the source via the `TerminalComponentModeler`, the objective runs a simulation and we compute the S11 spectrum and flux into a band of angles above the antenna. S11 is a measure of how much energy is reflected and the radiated flux over a set of angles gives us a good figure of merit for directivity. We optimize the antenna to direct the radiated power at 0 degrees, directly outward from the antenna plane. The two figures are combined together into one by computing reflection efficiency as $1 - |S_{11}|^2$ and multiplying by the sum of the flux over a narrow band of angles around 0 degrees normalized by the initial flux in each of these angles.\n",
+ "\n",
+ ""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def compute_poynting_and_s11(\n",
+ " patch_params,\n",
+ " patch_params_to_boxes,\n",
+ " sub_x_bounds,\n",
+ " sub_y_bounds,\n",
+ " optimization_monitors,\n",
+ " opt_freqs,\n",
+ "):\n",
+ " \"\"\"\n",
+ " Computes the Poynting flux for an antenna. The antenna parameters are specified in and\n",
+ " `patch_params` and a function `patch_params_to_boxes` convert these parameters into\n",
+ " simulation objects. These objects are inserted into a base simulation defined by\n",
+ " `sub_x_bounds` and `sub_y_bounds`. After setting up the antenna simulation including\n",
+ " the `optimization_monitors`, the far fields are computed\n",
+ " are,\n",
+ " the antenna simulation is run with the Poynting flux being computed with angular components\n",
+ "\n",
+ " \"\"\"\n",
+ " base_sim = create_base_sim(sub_x_bounds, sub_y_bounds)\n",
+ "\n",
+ " directivity_monitors = [\n",
+ " monitor for monitor in optimization_monitors if isinstance(monitor, td.DirectivityMonitor)\n",
+ " ]\n",
+ " non_directivity_monitors = [\n",
+ " monitor\n",
+ " for monitor in optimization_monitors\n",
+ " if not isinstance(monitor, td.DirectivityMonitor)\n",
+ " ]\n",
+ "\n",
+ " # Add antenna patches to simulation\n",
+ " sim_with_patches = create_simulation_with_patches(\n",
+ " base_sim, patch_params_to_boxes(patch_params), non_directivity_monitors\n",
+ " )\n",
+ "\n",
+ " # Create the `TerminalComponentModeler` to add the source and get the simulation we can run\n",
+ " # to evaluate the antenna performance\n",
+ " modeler_freqs = sorted(list(set(list(opt_sim_freqs) + list(opt_freqs))))\n",
+ "\n",
+ " modeler = create_modeler(\n",
+ " sim_with_patches, modeler_freqs, radiation_monitors=directivity_monitors\n",
+ " )\n",
+ "\n",
+ " # Run the simulations for the antenna.\n",
+ " smatrix_data = run(modeler, task_name=\"smatrix\", verbose=False)\n",
+ "\n",
+ " radiation_monitors = [m for m in optimization_monitors if m.name == \"radiation\"]\n",
+ " far_field_monitor = [m for m in optimization_monitors if m.name == \"far_field\"][0]\n",
+ " # Set up a `FieldProjector` based on the 'radiation' monitor near the patch\n",
+ " projector = td.FieldProjector.from_near_field_monitors(\n",
+ " sim_data=smatrix_data.data[\"lumped_port\"],\n",
+ " near_monitors=radiation_monitors,\n",
+ " normal_dirs=[\"+\"], # we are projecting along the +z direction\n",
+ " )\n",
+ " # Project this near field into the subset of far field components\n",
+ " # specified by the 'far_field' monitor\n",
+ " radiation_data = projector.project_fields(far_field_monitor)\n",
+ " poynting_flux = np.abs(\n",
+ " np.real(\n",
+ " 0.5\n",
+ " * (\n",
+ " radiation_data.Etheta * np.conj(radiation_data.Hphi)\n",
+ " - radiation_data.Ephi * np.conj(radiation_data.Htheta)\n",
+ " )\n",
+ " )\n",
+ " )\n",
+ "\n",
+ " return poynting_flux, smatrix_data.smatrix()\n",
+ "\n",
+ "\n",
+ "def objective_fn(\n",
+ " patch_params,\n",
+ " patch_params_to_boxes,\n",
+ " sub_x_bounds,\n",
+ " sub_y_bounds,\n",
+ " optimization_monitors,\n",
+ " opt_freqs,\n",
+ " poynting_flux_initial,\n",
+ "):\n",
+ " \"\"\"\n",
+ " Objective function for an antenna optimization that computes a product of the reflection\n",
+ " efficiency and the increased flux in a set of optimization angles compared to the initial design.\n",
+ " \"\"\"\n",
+ "\n",
+ " def weights_from_merit(merit):\n",
+ " \"\"\"\n",
+ " Computes performance based weights that sum to a total weight of 1.\n",
+ " For a given frequency, the weights are inversely tied to the performance\n",
+ " so that figures of merit that are lagging get favored more than those that\n",
+ " are leading.\n",
+ " \"\"\"\n",
+ "\n",
+ " weights = (2.0 / len(merit)) - (merit**2 / anp.sum(merit**2))\n",
+ " clip_weights = anp.maximum(weights, 0.0)\n",
+ "\n",
+ " inv_weights = 1.0 / anp.sum(clip_weights)\n",
+ " renorm_weights = clip_weights * inv_weights\n",
+ "\n",
+ " return renorm_weights\n",
+ "\n",
+ " poynting_flux, smatrix = compute_poynting_and_s11(\n",
+ " patch_params,\n",
+ " patch_params_to_boxes,\n",
+ " sub_x_bounds,\n",
+ " sub_y_bounds,\n",
+ " optimization_monitors,\n",
+ " opt_freqs,\n",
+ " )\n",
+ "\n",
+ " fom_by_freq = []\n",
+ " for freq in opt_freqs:\n",
+ " directivity_metric = np.sum(poynting_flux.sel(f=freq).data) / np.sum(\n",
+ " poynting_flux_initial.sel(f=freq).data\n",
+ " )\n",
+ "\n",
+ " s11 = np.abs(smatrix.data.isel(port_out=0, port_in=0).sel(f=freq).data)\n",
+ "\n",
+ " fom = (1 - np.abs(s11) ** 2) * directivity_metric\n",
+ " fom_by_freq.append(fom)\n",
+ "\n",
+ " fom_by_freq = anp.array(fom_by_freq)\n",
+ "\n",
+ " if len(fom_by_freq) > 1:\n",
+ " weights = weights_from_merit(fom_by_freq) # dynamic optimization weights\n",
+ " return anp.sum(weights * fom_by_freq)\n",
+ " else:\n",
+ " return anp.sum(fom_by_freq)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We then create a helper function to convert the patch width and height parameters to the antenna geometry for a single patch. To check out setup, we plot what the resulting antenna will look like when inserted into a simulation."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
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15:51:42 EDT WARNING: ℹ️ ⚠️ RF simulations are subject to new license requirements\n",
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WARNING: ℹ️ ⚠️ RF simulations are subject to new license \n",
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WARNING: ℹ️ ⚠️ RF simulations are subject to new license \n",
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WARNING: ℹ️ ⚠️ RF simulations are subject to new license \n",
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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "def params_to_boxes_single_patch(patch_params):\n",
+ " \"\"\"Convert single patch parameters into patch geometry.\"\"\"\n",
+ "\n",
+ " patch_wh = patch_params[0:2]\n",
+ "\n",
+ " main_patch = td.Box.from_bounds(\n",
+ " [-0.5 * patch_wh[0], 0.0, sub_z / 2.0], [0.5 * patch_wh[0], patch_wh[1], sub_z / 2.0]\n",
+ " )\n",
+ "\n",
+ " return [main_patch]\n",
+ "\n",
+ "\n",
+ "# set an initial width and height for the patch\n",
+ "patch_init_width = 12.45 * mm\n",
+ "patch_init_height = 16 * mm\n",
+ "patch_init_wh = anp.array([patch_init_width, patch_init_height])\n",
+ "\n",
+ "# set the min/max for the patch width and height to use to bound these parameters in the optimization\n",
+ "patch_min_width = 8 * mm\n",
+ "patch_min_height = 8 * mm\n",
+ "\n",
+ "patch_max_width = 18 * mm\n",
+ "patch_max_height = 24 * mm\n",
+ "\n",
+ "# plot the patch and mesh of the initial structure to visually inspect geometry before starting optimization\n",
+ "plot_structures_and_mesh(\n",
+ " params_to_boxes_single_patch(patch_init_wh), sub_x_single_patch, sub_y_single_patch\n",
+ ")\n",
+ "# Helper function for evaluating antenna performance for a single patch\n",
+ "evaluate_single_antenna = lambda patch_wh, eval_s_params_freqs, opt_freqs: (\n",
+ " evaluate_antenna(\n",
+ " params_to_boxes_single_patch(patch_wh),\n",
+ " eval_s_params_freqs,\n",
+ " opt_freqs,\n",
+ " sub_x_single_patch,\n",
+ " sub_y_single_patch,\n",
+ " )\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now, we set up and run the optimization. We create the monitors to capture the far field radiation and solve for the initial flux into the target radiation angular components. Then, we configure the optimizer which we can also load from a saved checkpoint by toggling the `restart_optimization` flag if we get interrupted in the middle. Finally, we run the optimization and capture the figure of merit and other interesting quantities along the way."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "
15:51:46 EDT Component modeler batch validation has been successful. \n",
+ "
19:42:19 EDT Component modeler batch validation has been successful. \n",
+ "
\n"
+ ],
+ "text/plain": [
+ "\u001b[2;36m19:42:19 EDT\u001b[0m\u001b[2;36m \u001b[0mComponent modeler batch validation has been successful. \n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# compute S11 and realized gain for the initial multi patch geometry\n",
+ "smatrix_data_init, theta, realized_gain_init = evaluate_multi_antenna(\n",
+ " init_params, freqs_s_params, opt_freqs_multi_patch\n",
+ ")\n",
+ "# compute S11 and realized gain for the optimized multi patch geometry\n",
+ "smatrix_data_final, theta, realized_gain_final = evaluate_multi_antenna(\n",
+ " patch_params, freqs_s_params, opt_freqs_multi_patch\n",
+ ")\n",
+ "\n",
+ "# plot the comparison in S11 and gain for the initial and optimized antennas for phi=0\n",
+ "plot_phi = 0.0\n",
+ "plot_antenna_comparison(\n",
+ " [smatrix_data_init.smatrix(), smatrix_data_final.smatrix()],\n",
+ " [smatrix_data_init.data, smatrix_data_final.data],\n",
+ " [realized_gain_init, realized_gain_final],\n",
+ " opt_freqs_multi_patch,\n",
+ " theta,\n",
+ " plot_phi,\n",
+ " sim_names=[\"before optimization\", \"after optimization\"],\n",
+ " plot_title=f\"Antenna Comparison (plot_phi=0)\",\n",
+ ")\n",
+ "\n",
+ "# plot the comparison in S11 and gain for the initial and optimized antennas for phi=pi / 2\n",
+ "plot_phi = np.pi / 2.0\n",
+ "plot_antenna_comparison(\n",
+ " [smatrix_data_init.smatrix(), smatrix_data_final.smatrix()],\n",
+ " [smatrix_data_init.data, smatrix_data_final.data],\n",
+ " [realized_gain_init, realized_gain_final],\n",
+ " opt_freqs_multi_patch,\n",
+ " theta,\n",
+ " plot_phi,\n",
+ " sim_names=[\"before optimization\", \"after optimization\"],\n",
+ " plot_title=f\"Antenna Comparison (plot_phi=\\u03a0 / 2)\",\n",
+ ")\n",
+ "\n",
+ "# Save results of optimization\n",
+ "np.save(\"misc/multi_patch_box_antenna_optimization.npy\", patch_params)"
+ ]
+ }
+ ],
+ "metadata": {
+ "description": "Patch antennas are widely used in wireless communication applications due to their simple design, ease of fabrication, and low profile. In this notebook, we will demonstrate how to use Tidy3D to simulate a rectangular patch antenna and compute key performance metrics. These include S-parameters using the TerminalComponentModeler, as well as directivity, axial ratio, and polarized far-field components using the DirectivityMonitor.",
+ "feature_image": "./img/PatchAntenna.png",
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "keywords": "patch antenna, directivity, axial ratio, S-parameters, 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": "3.13.2"
+ },
+ "title": "How to compute directivity and S-parameters of patch antenna using Tidy3D FDTD"
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/RFAutograd1RectangularPatchAntennaSplit1.ipynb b/RFAutograd1RectangularPatchAntennaSplit1.ipynb
new file mode 100644
index 00000000..be90e4d0
--- /dev/null
+++ b/RFAutograd1RectangularPatchAntennaSplit1.ipynb
@@ -0,0 +1,1513 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "editable": true,
+ "slideshow": {
+ "slide_type": ""
+ },
+ "tags": []
+ },
+ "source": [
+ "# Adjoint Optimization of Rectangular Patch Antennas\n",
+ "\n",
+ "This notebook demonstrates the use of inverse design for the optimization of antennas. A simple rectangular patch antenna, as explored in our [Introduction to Antenna Simulation](https://www.flexcompute.com/tidy3d/examples/notebooks/AntennaCharacteristics/) notebook, is primarily defined by two parameters: its width and height. If you aren't familiar with it already, we strongly recommend going through [Introduction to Antenna Simulation](https://www.flexcompute.com/tidy3d/examples/notebooks/AntennaCharacteristics/) before this one as we will assume knowledge of the RF modeling workflow in tidy3d in this notebook. These are tuned to make the antenna resonate at a desired frequency and match the impedance of the feed line. Usually, these parameters can be found via intuitive design techniques and numerical formulas. However, in this example, we show a different strategy to tuning the antenna parameters. This notebook demonstrates gradient-based RF optimization in Tidy3D. This technique, enabled by the adjoint method and automatic differentiation (autograd), allows for the efficient and simultaneous optimization of all geometric parameters, also known as inverse design.\n",
+ "\n",
+ "We illustrate this process through the straightforward optimization to find the ideal width and height of a rectangular patch antenna for a single target frequency. In future notebooks, we will build on this example and show more intricate patch antenna designs that take advantage of inverse design to simultaneously tune large numbers of geometric parameters to meet user-defined metrics with high performance. The optimization geometry is shown below. The antenna consists of a metallic patch on a substrate with a ground plane, is excited by an offset feed line, and radiates into free space. \n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ "
"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "editable": true,
+ "slideshow": {
+ "slide_type": ""
+ },
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# Tidy3d import\n",
+ "import tidy3d as td\n",
+ "\n",
+ "# External modules needed for this notebook\n",
+ "import numpy as np\n",
+ "import autograd.numpy as anp\n",
+ "from autograd import value_and_grad\n",
+ "import optax\n",
+ "import os\n",
+ "import pickle\n",
+ "\n",
+ "# Tidy3d plugin import\n",
+ "import tidy3d.plugins.smatrix as smatrix\n",
+ "from tidy3d.plugins.microwave import rf_material_library\n",
+ "from tidy3d.web import run\n",
+ "\n",
+ "# Libraries and configuration for printing and display\n",
+ "import matplotlib.pyplot as plt\n",
+ "import matplotlib.patches as patches\n",
+ "from rich.console import Console\n",
+ "from rich.text import Text\n",
+ "\n",
+ "# Setup console and printing parameters for rich printing during optimization loops\n",
+ "console = Console()\n",
+ "print_decimal_places = 2 # how many decimal places to use in printing\n",
+ "print_iteration_frequency = 5 # how often to print optimization progress"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Configuration and Parameters\n",
+ "\n",
+ "In this first section, we set up a variety of parameters for the optimization, including the frequency bands (specified in Hz) and resonance target as well as useful geometric parameters. Similar to other RF examples, we introduce a scaling factor to convert the default unit in Tidy3D of micrometers to millimeters (mm), which is more commonly used in antenna simulations. Thus, the default unit when looking at constants in this notebook is millimeters."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# length scales and geometric parameters for optimization\n",
+ "mm = 1e3 # mm scaling\n",
+ "\n",
+ "# feedline parameters\n",
+ "feed_x = 2.46 * mm # width of feed\n",
+ "feed_y = 20 * mm # length of feed\n",
+ "feed_offset = 2.09 * mm # feed offset from center of patch\n",
+ "\n",
+ "# substrate geometric parameters\n",
+ "sub_width = 23.34 * mm\n",
+ "sub_height_extension = 25 * mm\n",
+ "\n",
+ "sub_x = [-0.5 * sub_width, 0.5 * sub_width]\n",
+ "sub_y = [-feed_y, sub_height_extension]\n",
+ "sub_z = 0.68 * mm\n",
+ "\n",
+ "# center and size for placing directivity and far field monitors to capture radiation from the\n",
+ "# antenna patch\n",
+ "directivity_center_x = np.mean(sub_x)\n",
+ "directivity_center_y = np.mean(sub_y)\n",
+ "directivity_size_x = 5 * mm + sub_x[1] - sub_x[0]\n",
+ "directivity_size_y = 5 * mm + sub_y[1] - sub_y[0]\n",
+ "\n",
+ "# frequency range (Hz) for simluating\n",
+ "freq_start = 7e9\n",
+ "freq_stop = 11e9\n",
+ "# simulation frequencies to cover enough bandwidth for evaluating and optimizing antennas\n",
+ "opt_sim_freqs = [8e9, 10e9]\n",
+ "\n",
+ "freq0 = 0.5 * (freq_start + freq_stop) # central frequency\n",
+ "wavelength0 = td.C_0 / freq0 # wavelength of centeral frequency in vacuum\n",
+ "\n",
+ "# frequencies for computing S-parameters of antennas\n",
+ "freqs_s_params = np.linspace(freq_start, freq_stop, 200)\n",
+ "\n",
+ "# frequencies for optimizing different\n",
+ "opt_freqs = [8.25e9] # single patch target frequency\n",
+ "\n",
+ "# set ranges of theta and phi to record directivity for\n",
+ "theta_directivity = np.linspace(-np.pi, np.pi, 201)\n",
+ "phi_directivity = np.linspace(0, np.pi, 101)\n",
+ "\n",
+ "# set the optimization theta, phi grid for enhancing directivity around theta=0\n",
+ "num_theta_opt_points = 12\n",
+ "theta_opt = theta_directivity[\n",
+ " (len(theta_directivity) // 2) - (num_theta_opt_points // 2) : (len(theta_directivity) // 2)\n",
+ " + (num_theta_opt_points // 2)\n",
+ " + 1\n",
+ "]\n",
+ "phi_opt = phi_directivity # use the full phi sweep, but limit the theta points to be centered around 0 degrees\n",
+ "\n",
+ "# materials for optimization\n",
+ "air = td.Medium() # set up the antennas so they radiate into air\n",
+ "# choose common PCB material, ArlonAD255C, from the RF material library to use as substrate\n",
+ "sub_medium = rf_material_library[\"AD255C\"][\"design\"]\n",
+ "PEC = td.PEC2D # thickness-free PEC medium for antenna patches, feed lines, and the ground plane"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Create Simulation Objects\n",
+ "\n",
+ "To prepare for the optimization, we need to make a base simulation that we can add the antenna geometry and excitation source to. This base simulation includes the ground plane and substrate, the two structures below the patch antenna. In this simulation, we also set up `LayerRefinementSpec` objects around our ground, substrate, and patch antenna layers. The fine mesh improvse accuracy of both the simulation and PEC gradients. As in other RF examples, the source will be added later via a `LumpedPort`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def create_base_sim():\n",
+ " \"\"\"\n",
+ " Creates the base simulation for evaluating and optimizing patch antennas. The base simulation includes the\n",
+ " substrate geometry, and ground plane. It also overrides a vertical slice of the simulation where the feed and\n",
+ " patch will be placed with a fine mesh override to ensure accurate simulation and gradients near the PEC boundaries.\n",
+ " \"\"\"\n",
+ " substrate_box = td.Box.from_bounds(\n",
+ " [sub_x[0], sub_y[0], -sub_z / 2.0],\n",
+ " [sub_x[1], sub_y[1], sub_z / 2.0],\n",
+ " )\n",
+ " # Define substrate structure\n",
+ " substrate = td.Structure(\n",
+ " geometry=substrate_box,\n",
+ " medium=sub_medium,\n",
+ " )\n",
+ "\n",
+ " # Define ground plane structure and assign the material by PEC\n",
+ " ground_plane = td.Structure(\n",
+ " geometry=substrate_box.updated_copy(\n",
+ " center=list(substrate_box.center[0:2]) + [-sub_z / 2.0],\n",
+ " size=list(substrate_box.size[0:2]) + [0],\n",
+ " ),\n",
+ " medium=PEC,\n",
+ " )\n",
+ "\n",
+ " # list of structures for the simulation arranged first by dielectric and then PEC to\n",
+ " # ensure PEC takes precedence at interfaces.\n",
+ " structures_list = [substrate, ground_plane]\n",
+ "\n",
+ " # PML wavelength at 10 GHz\n",
+ " wl_pml = wavelength0\n",
+ "\n",
+ " # quarter wavelength (at 10 GHz) padding on each side\n",
+ " sim_x_size = sub_x[1] - sub_x[0] + wl_pml / 2.0\n",
+ " sim_y_size = sub_y[1] - sub_y[0] + wl_pml / 2.0\n",
+ " sim_y_center = np.mean(sub_y)\n",
+ "\n",
+ " sim_z_max = sub_z + 1.5 * wavelength0\n",
+ " sim_z_min = sub_z - 0.5 * wavelength0\n",
+ " sim_z_center = 0.5 * (sim_z_max + sim_z_min)\n",
+ " sim_z_size = sim_z_max - sim_z_min\n",
+ "\n",
+ " # set a fine mesh for the corner refinement based on the center wavelength\n",
+ " dl = wavelength0 / 200.0\n",
+ " mesh_override_vertical_padding = 1 * mm\n",
+ "\n",
+ " layer_refinement_ground = td.LayerRefinementSpec.from_structures(\n",
+ " axis=2,\n",
+ " min_steps_along_axis=3,\n",
+ " structures=[ground_plane])\n",
+ "\n",
+ " layer_refinement_patch = td.LayerRefinementSpec(\n",
+ " center=(0, sim_y_center, sub_z / 2),\n",
+ " size=(td.inf, td.inf, mesh_override_vertical_padding),\n",
+ " axis=2,\n",
+ " min_steps_along_axis=3,\n",
+ " corner_refinement=td.GridRefinement(dl=dl, num_cells=3))\n",
+ "\n",
+ " layer_refinement_substrate = td.LayerRefinementSpec.from_structures(\n",
+ " axis=2,\n",
+ " structures=[substrate],\n",
+ " min_steps_along_axis=3)\n",
+ " \n",
+ " # Truncate the computational domain by PMLs\n",
+ " boundary_spec = td.BoundarySpec(\n",
+ " x=td.Boundary.pml(),\n",
+ " y=td.Boundary.pml(),\n",
+ " z=td.Boundary.pml(),\n",
+ " )\n",
+ "\n",
+ " # Create the simulation object\n",
+ " base_sim = td.Simulation(\n",
+ " center=[0.0, sim_y_center, sim_z_center],\n",
+ " size=[sim_x_size, sim_y_size, sim_z_size],\n",
+ " grid_spec=td.GridSpec.auto(\n",
+ " min_steps_per_wvl=20, # largest cell size is set to 20 cells per smallest wavelength.\n",
+ " wavelength=td.C_0 / freq_stop, # smallest wavelength to resolve\n",
+ " layer_refinement_specs=[layer_refinement_ground, layer_refinement_substrate, layer_refinement_patch],\n",
+ " ),\n",
+ " structures=structures_list,\n",
+ " sources=[], # sources will be added by TerminalComponentModeler\n",
+ " monitors=[],\n",
+ " run_time=70 * (substrate.geometry.size[1] / td.C_0),\n",
+ " boundary_spec=boundary_spec,\n",
+ " plot_length_units=\"mm\", # this option will make plots default to units of millimeters.\n",
+ " )\n",
+ " \n",
+ " return base_sim"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In addition to the base simulation creation, we set up a function to add a feed line and PEC patches to a simulation object. Later, this will allow us to create an antenna with an arbitrary list of patches and insert it into our base simulation."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def create_simulation_with_patches(base_sim, patch_boxes, monitors):\n",
+ " \"\"\"\n",
+ " Creates a new simulation from the base simulation that includes PEC patches for all of the Box\n",
+ " objects in patch_boxes as well as adds a feed line.\n",
+ " \"\"\"\n",
+ " patches = []\n",
+ " for patch_box in patch_boxes:\n",
+ " patches.append(td.Structure(geometry=patch_box, medium=PEC))\n",
+ "\n",
+ " feed_geometry = td.Box.from_bounds(\n",
+ " [feed_offset - 0.5 * feed_x, -feed_y, sub_z / 2], [feed_offset + 0.5 * feed_x, 0, sub_z / 2]\n",
+ " )\n",
+ "\n",
+ " feed = td.Structure(geometry=feed_geometry, medium=PEC)\n",
+ "\n",
+ " updated_sim = base_sim.updated_copy(\n",
+ " structures=list(base_sim.structures) + [feed] + patches,\n",
+ " monitors=list(base_sim.monitors) + monitors,\n",
+ " )\n",
+ "\n",
+ " return base_sim.updated_copy(\n",
+ " structures=list(base_sim.structures) + [feed] + patches,\n",
+ " monitors=list(base_sim.monitors) + monitors,\n",
+ " )"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Finally, we set up a function to create a `LumpedPort` and `TerminalComponentModeler` that will generate the input excitation to the antenna. We can specify an impedance for the port as well as the desired frequencies for the resulting simulation. The `TerminalComponentModeler` will set up the simulations we need to evaluate and optimize the antenna."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def create_modeler(simulation, frequencies, radiation_monitors=(), port_impedance=50):\n",
+ " \"\"\"\n",
+ " Creates the LumpedPort and TerminalComponentModeler based on a `simulation`, the desired\n",
+ " simulation `frequencies`, `radiation_monitors` for computing directivity, and a `port_impedance`.\n",
+ " \"\"\"\n",
+ " # Setup a LumpedPort for the TerminalComponentModeler, which is needed\n",
+ " # to end the port with a matched load as well as providing a source for the simulation.\n",
+ " port = smatrix.LumpedPort(\n",
+ " center=[feed_offset, -feed_y, 0],\n",
+ " size=[feed_x, 0, sub_z],\n",
+ " voltage_axis=2,\n",
+ " name=\"lumped_port\",\n",
+ " impedance=port_impedance,\n",
+ " )\n",
+ "\n",
+ " # We integrate the base simulation with the LumpedPort using the TerminalComponentModeler.\n",
+ " # This allows us to compute scattering parameters and extract any additional data needed from the simulation.\n",
+ " modeler = smatrix.TerminalComponentModeler(\n",
+ " simulation=simulation,\n",
+ " ports=[port],\n",
+ " freqs=frequencies,\n",
+ " remove_dc_component=False, # include DC component for more accuracy at low frequencies\n",
+ " radiation_monitors=radiation_monitors,\n",
+ " )\n",
+ "\n",
+ " return modeler"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Setup Plotting and Evaluation Functions\n",
+ "\n",
+ "Here, we set up some helper functions to aid in plotting antenna geometry and evaluating antenna characteristics before and after optimizations. First, we set up a function to plot the antenna structure and feed line along with the surrounding mesh. Near PEC edges, especially when computing gradients, we recommend using a fine mesh. In this function, we can also observe the location where the input source is fed into the antenna to confirm it is at the end of the feed line. **Note: the plotting functions below are useful for visualizations in this notebook, but their internals are not crucial to the understanding of how to set up and run gradient based RF optimizations in tidy3d. Feel free to skim this section and skip to the next section (\"Demonstrating Gradient Based Optimization of a Patch Antenna\") whenever you're ready.**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def plot_structures_and_mesh(patch_boxes):\n",
+ " \"\"\"Plots the antenna structure and surrounding mesh to ensure it looks as expected before running simulations.\"\"\"\n",
+ " base_sim = create_base_sim()\n",
+ "\n",
+ " no_additional_monitors = []\n",
+ " sim_with_patches = create_simulation_with_patches(base_sim, patch_boxes, no_additional_monitors)\n",
+ "\n",
+ " fig, ax = plt.subplots()\n",
+ "\n",
+ " # examine the structure and mesh in the x-y plane\n",
+ " sim_with_patches.plot(\n",
+ " z=sub_z / 2,\n",
+ " ax=ax,\n",
+ " monitor_alpha=0.2,\n",
+ " )\n",
+ " sim_with_patches.plot_grid(z=sub_z / 2, ax=ax)\n",
+ "\n",
+ " plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Second, we set up a function that can calculate `S11` and realized antenna gain for each polarization. `S11` is the voltage reflection coefficient and $|S_{11}|^2$ is the power reflection coefficient or in other words, the reflected power divided by the input power. A good antenna will have a small `S11` at its operating frequencies. `S11` is plotted in dB and thus at resonance, we will see a large, negative value corresponding to low reflection. Before and after an optimization, this is one way to evaluate how well the optimization tuned the geometry for the correct frequencies. In the realized gain plot, we can see how efficiently energy is radiating from the antenna and in what direction when compared to an isotropic radiator. For simplicity and to keep plots less crowded, we collect the realized gain for the optimization frequencies while we compute `S11` value over a broad spectrum."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def evaluate_antenna(patch_params, eval_s_params_freqs):\n",
+ " \"\"\"\n",
+ " Evaluate the S11 parameter and realized gain in both polarizations for the given antenna geometry.\n",
+ " The S11 is calculated across a broad spectrum while the realized gain is only computed for the opt_freqs.\n",
+ " \"\"\"\n",
+ " patch_boxes = params_to_boxes(patch_params)\n",
+ "\n",
+ " base_sim = create_base_sim()\n",
+ " no_additional_monitors = []\n",
+ " sim_with_patches = create_simulation_with_patches(base_sim, patch_boxes, no_additional_monitors)\n",
+ "\n",
+ " monitor_directivity = td.DirectivityMonitor(\n",
+ " center=[directivity_center_x, directivity_center_y, 0],\n",
+ " size=(\n",
+ " directivity_size_x,\n",
+ " directivity_size_y,\n",
+ " 4 * mm,\n",
+ " ),\n",
+ " freqs=opt_freqs,\n",
+ " name=\"directivity\",\n",
+ " phi=list(phi_directivity),\n",
+ " theta=list(theta_directivity),\n",
+ " far_field_approx=True,\n",
+ " )\n",
+ " \n",
+ " # we only need whatever frequencies are unique in these two lists to have all the data we need for computing\n",
+ " # S11 and gain\n",
+ " eval_freqs = np.unique(list(eval_s_params_freqs) + list(opt_freqs))\n",
+ "\n",
+ " modeler = create_modeler(\n",
+ " sim_with_patches, eval_freqs, radiation_monitors=[monitor_directivity]\n",
+ " )\n",
+ " smatrix_data = run(modeler, task_name=\"smatrix\", verbose=False)\n",
+ "\n",
+ " antenna_parameters_freq = smatrix_data.get_antenna_metrics_data(monitor_name=\"directivity\")\n",
+ " partial_realized_gain = antenna_parameters_freq.partial_realized_gain(pol_basis=\"linear\")\n",
+ "\n",
+ " return smatrix_data, partial_realized_gain"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Given the optimization results, we can plot the comparison of two antenna simulations using the `plot_antenna_comparison` function below. We can easily compare the initial and final optimization states to see how well the resulting antenna is performing in our desired metrics. During the optimization, we will also periodically save the `S11` and gain of the antenna. The function `plot_antenna_evolution` allows us to plot these metrics out so we can see how performance evolved over the course of the optimization."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def plot_antenna_comparison(\n",
+ " s_matrix_list,\n",
+ " sim_data_list,\n",
+ " partial_realized_gain_list,\n",
+ " plot_phi,\n",
+ " plot_title=\"Antenna Simulation Comparison\",\n",
+ " sim_names=None,\n",
+ " single_color_gain_plots=False,\n",
+ " savefig_fname=None,\n",
+ "):\n",
+ " \"\"\"\n",
+ " Plots a comparison of two sets of simulation data.\n",
+ "\n",
+ " Args:\n",
+ " s_matrix_list: List of S-matrix objects [s_matrix_sim1, s_matrix_sim2].\n",
+ " sim_data_list: List of sim_data objects [sim_data_sim1, sim_data_sim2],\n",
+ " where each sim_data contains a \"radiation\" DirectivityMonitor output.\n",
+ " partial_realized_gain_list: List of realized gain in linear polarization for each simulation.\n",
+ " plot_phi: The phi value to select in the realized gain data for plotting.\n",
+ " plot_title: Optional title to specify for plot (default is \"Antenna Simulation Comparison\").\n",
+ " sim_names: Optional list of names for simulations for clearer legends.\n",
+ " single_color_gain_plots: Optional choice to use a single color for all the frequencies\n",
+ " in the gain plot (True) or have each frequency a different\n",
+ " color (False). Default is False.\n",
+ " savefig_fname: An optional filename to save the resulting figure\n",
+ " \"\"\"\n",
+ "\n",
+ " num_simulations = len(s_matrix_list)\n",
+ " if num_simulations != 2 or len(sim_data_list) != 2:\n",
+ " print(\"Warning: This function is designed to compare exactly two simulations.\")\n",
+ "\n",
+ " alphas = [0.5, 1.0] # Alpha for sim1, sim2\n",
+ " if sim_names is None or len(sim_names) != num_simulations:\n",
+ " sim_names = [f\"Sim {i + 1}\" for i in range(num_simulations)]\n",
+ "\n",
+ " grid_spec_cols = 3 # S11 and realized gain for each polarization side-by-side\n",
+ " fig_width = grid_spec_cols * 4.5\n",
+ " fig_height = 6.5\n",
+ "\n",
+ " num_rows = 1\n",
+ "\n",
+ " fig = plt.figure(figsize=(fig_width, fig_height), constrained_layout=True)\n",
+ " gs = fig.add_gridspec(num_rows, grid_spec_cols)\n",
+ " axs_list = []\n",
+ "\n",
+ " # plot the S11 parameter comparison for each simulation\n",
+ " ax_s11 = fig.add_subplot(gs[0, 0])\n",
+ " axs_list.append(ax_s11)\n",
+ " ax_s11.set_title(\"$S_{11}$ Coefficient\")\n",
+ " ax_s11.set_xlabel(\"Frequency (GHz)\")\n",
+ " ax_s11.set_ylabel(\"$|S_{11}|^2$ (dB)\")\n",
+ "\n",
+ " for sim_idx in range(num_simulations):\n",
+ " s_matrix = s_matrix_list[sim_idx]\n",
+ " current_alpha = alphas[sim_idx]\n",
+ " sim_label_name = sim_names[sim_idx]\n",
+ "\n",
+ " s11_freqs_ghz = s_matrix.data.coords[\"f\"] / 1e9\n",
+ " s11_data_selection = s_matrix.data.isel(port_out=0, port_in=0)\n",
+ "\n",
+ " s11_values_flat = s11_data_selection.values.flatten()\n",
+ " s11_values_db = 20 * np.log10(np.abs(s11_values_flat))\n",
+ " ax_s11.plot(\n",
+ " s11_freqs_ghz,\n",
+ " s11_values_db,\n",
+ " alpha=current_alpha,\n",
+ " label=f\"{sim_label_name})\",\n",
+ " )\n",
+ "\n",
+ " impedances_norm = (1 + s11_values_flat) / (1 - s11_values_flat)\n",
+ "\n",
+ " for opt_freq in opt_freqs:\n",
+ " ax_s11.axvline(x=opt_freq / 1e9, color=\"k\", linestyle=\"--\")\n",
+ "\n",
+ " ax_s11.set_ylim(-25, 2)\n",
+ " ax_s11.grid(True)\n",
+ " ax_s11.legend()\n",
+ "\n",
+ " # for each linear polarization component, plot the realized gain for each linear polarization\n",
+ " polarization_components = [\"Gtheta\", \"Gphi\"]\n",
+ "\n",
+ " for pol_idx, pol_component in enumerate(polarization_components):\n",
+ " ax_polar = fig.add_subplot(gs[0, 1 + pol_idx], projection=\"polar\")\n",
+ " axs_list.append(ax_polar)\n",
+ " ax_polar.set_title(f\"Realized Gain for {pol_component}\")\n",
+ "\n",
+ " ax_polar.set_theta_direction(-1)\n",
+ " ax_polar.set_theta_offset(np.pi / 2.0)\n",
+ " ax_polar.grid(True)\n",
+ " ax_polar.set_rlabel_position(22.5)\n",
+ "\n",
+ " overall_max_gain = -np.inf\n",
+ " color_cycle = plt.rcParams[\"axes.prop_cycle\"].by_key()[\"color\"]\n",
+ "\n",
+ " for freq_idx, eval_freq in enumerate(opt_freqs):\n",
+ " base_color = color_cycle[freq_idx % len(color_cycle)]\n",
+ " if single_color_gain_plots:\n",
+ " base_color = color_cycle[0]\n",
+ " freq_label_for_legend = f\"{eval_freq / 1e9:.2f} GHz\"\n",
+ "\n",
+ " # Plot data for each simulation for the current frequency\n",
+ " for sim_idx in range(num_simulations):\n",
+ " sim_data = sim_data_list[sim_idx]\n",
+ " current_alpha = alphas[sim_idx]\n",
+ "\n",
+ " if single_color_gain_plots:\n",
+ " base_color = color_cycle[sim_idx]\n",
+ "\n",
+ " realized_gain_data = partial_realized_gain_list[sim_idx][pol_component].sel(\n",
+ " f=eval_freq, phi=plot_phi, method=\"nearest\"\n",
+ " )\n",
+ "\n",
+ " gain_values_for_plot = realized_gain_data.squeeze().values\n",
+ "\n",
+ " current_max_val = np.max(gain_values_for_plot)\n",
+ " if current_max_val > overall_max_gain:\n",
+ " overall_max_gain = current_max_val\n",
+ "\n",
+ " # Label only the second simulation's line (alpha=1.0) for the legend entry of this frequency\n",
+ " label_to_use = f\"{freq_label_for_legend} ({sim_names[sim_idx]})\"\n",
+ " ax_polar.plot(\n",
+ " theta_directivity,\n",
+ " gain_values_for_plot,\n",
+ " color=base_color,\n",
+ " alpha=current_alpha,\n",
+ " label=label_to_use,\n",
+ " )\n",
+ "\n",
+ " ax_polar.set_rlim(0, overall_max_gain * 1.1 if overall_max_gain > 0 else 1.0)\n",
+ " ax_polar.legend(title=\"Frequency (GHz)\", loc=\"best\", fontsize=\"small\")\n",
+ "\n",
+ " fig.suptitle(plot_title, fontsize=16)\n",
+ "\n",
+ " if savefig_fname:\n",
+ " plt.savefig(savefig_fname)\n",
+ "\n",
+ " plt.show()\n",
+ "\n",
+ "def plot_antenna_evolution(s11_sq_dB, s11_f, partial_realized_gain, gain_freqs):\n",
+ " \"\"\"Plots the evolution of the antenna S11 and and realized gain throughout an optimization.\n",
+ " Args:\n",
+ " s11_sq_dB: List of |S11|^2 (in dB) at each point in optimization. The length should match the\n",
+ " length of `partial_realized_gain`\n",
+ " s11_f: Frequencies for each S11 array\n",
+ " partial_realized_gain: List of realized gain objects broken into linear polarization. The length\n",
+ " of this list should match the length of `s11_sq_dB`\n",
+ " gain_freqs: List of frequencies for each realized gain object.\n",
+ " \"\"\"\n",
+ " num_lines = len(s11_sq_dB)\n",
+ "\n",
+ " alphas = np.linspace(0.25, 1.0, num_lines)\n",
+ "\n",
+ " fig_width = 13.5\n",
+ " fig_height = 6.5\n",
+ "\n",
+ " fig = plt.figure(figsize=(fig_width, fig_height), constrained_layout=True)\n",
+ " num_rows = 1\n",
+ " grid_spec_cols = 3\n",
+ " gs = fig.add_gridspec(num_rows, grid_spec_cols)\n",
+ " axs_list = []\n",
+ "\n",
+ " ax_s11 = fig.add_subplot(gs[0, 0])\n",
+ " axs_list.append(ax_s11)\n",
+ " ax_s11.set_title(\"$S_{11}$ Coefficient\")\n",
+ " ax_s11.set_xlabel(\"Frequency (GHz)\")\n",
+ " ax_s11.set_ylabel(\"$|S_{11}|^2$ (dB)\")\n",
+ "\n",
+ " color_cycle = plt.rcParams[\"axes.prop_cycle\"].by_key()[\"color\"]\n",
+ "\n",
+ " for idx in range(0, num_lines):\n",
+ " ax_s11.plot(s11_f, s11_sq_dB[idx], color=color_cycle[0], linewidth=1.5, alpha=alphas[idx])\n",
+ "\n",
+ " ax_s11.set_ylim(-25, 2)\n",
+ " ax_s11.grid(True)\n",
+ "\n",
+ " def add_polar_sequence(grid_col, pol_component, title):\n",
+ " ax_polar = fig.add_subplot(gs[0, grid_col], projection=\"polar\")\n",
+ " axs_list.append(ax_polar)\n",
+ " ax_polar.set_title(f\"Realized Gain for {pol_component}\")\n",
+ "\n",
+ " ax_polar.set_theta_direction(-1)\n",
+ " ax_polar.set_theta_offset(np.pi / 2.0)\n",
+ " ax_polar.grid(True)\n",
+ " ax_polar.set_rlabel_position(22.5)\n",
+ "\n",
+ " for idx in range(0, num_lines):\n",
+ " partial_realized_gain_batch = partial_realized_gain[idx][pol_component]\n",
+ " partial_realized_gain_batch = np.reshape(\n",
+ " partial_realized_gain_batch, (len(gain_freqs), len(theta_directivity))\n",
+ " )\n",
+ "\n",
+ " for freq_idx in range(0, len(gain_freqs)):\n",
+ " gain_values_for_plot = partial_realized_gain_batch[freq_idx]\n",
+ "\n",
+ " ax_polar.plot(\n",
+ " theta_directivity, gain_values_for_plot, color=color_cycle[freq_idx], alpha=alphas[idx]\n",
+ " )\n",
+ "\n",
+ " ax_polar.set_title(title)\n",
+ "\n",
+ " add_polar_sequence(1, \"Gtheta\", f\"Realized Gain\\n(Gtheta), phi=0\")\n",
+ " add_polar_sequence(2, \"Gphi\", f\"Realized Gain\\n(Gphi), phi=0\")\n",
+ "\n",
+ " plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We set up a function to plot the figure of merit trajectory of the optimization and compare the initial and final antenna geometries. This is an indication of how well the optimization worked as well as a demonstration of the overall change that occurred over the course of the optimization."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def plot_optimization_results(\n",
+ " figure_of_merit, feed_rect, init_rectangles, final_rectangles, savefig_fname=None\n",
+ "):\n",
+ " \"\"\"\n",
+ " Plots a main rectangle and two lists of other rectangles with specified styles.\n",
+ "\n",
+ " Args:\n",
+ " figure_of_merit: Figure of merit for each iteration during the optimization.\n",
+ " feed_rect: The rectangular corresponding to the feed line.\n",
+ " init_rectangles: A list of rectangles corresponding to the initial state\n",
+ " of the optimization.\n",
+ " final_rectangles: A list of rectangles corresponding to the final condition\n",
+ " of the optimization.\n",
+ " savefig_fname: Optional filename to save the resulting figure.\n",
+ "\n",
+ " \"\"\"\n",
+ " fig, ax = plt.subplots(1, 2)\n",
+ " ax[1].set_aspect(\"equal\", adjustable=\"box\")\n",
+ "\n",
+ " all_rects_params = [] # to store (x_bl, y_bl, width, height) for limit calculation\n",
+ "\n",
+ " # helper function to add a rectangle and collect its parameters\n",
+ " def add_rectangle_to_plot(\n",
+ " rect_obj, facecolor, alpha=1.0, edgecolor=\"black\", linestyle=\"solid\", legend=None\n",
+ " ):\n",
+ " \"\"\"\n",
+ " Adds a single rectangle to the plot and collects its parameters.\n",
+ "\n",
+ " Args:\n",
+ " rect_obj: The rectangle object with 'center' and 'size'.\n",
+ " facecolor: The face color of the rectangle.\n",
+ " alpha: Optional transparency of the rectangle.\n",
+ " edgecolor: Optional edge color of the rectangle.\n",
+ " linestyle: Optional line style of the rectangle's border.\n",
+ " legend: Optional legend entry to use for this rectangle.\n",
+ " \"\"\"\n",
+ " center_x, center_y, _ = (coord / mm for coord in rect_obj.center)\n",
+ " width, height, _ = (sz / mm for sz in rect_obj.size)\n",
+ "\n",
+ " # Calculate bottom-left corner coordinates\n",
+ " bottom_left_x = center_x - width / 2\n",
+ " bottom_left_y = center_y - height / 2\n",
+ "\n",
+ " all_rects_params.append((bottom_left_x, bottom_left_y, width, height))\n",
+ "\n",
+ " rect_patch = patches.Rectangle(\n",
+ " (bottom_left_x, bottom_left_y),\n",
+ " width,\n",
+ " height,\n",
+ " facecolor=facecolor,\n",
+ " alpha=alpha,\n",
+ " edgecolor=edgecolor,\n",
+ " linestyle=linestyle,\n",
+ " linewidth=1, # Default linewidth for borders\n",
+ " label=legend,\n",
+ " )\n",
+ " ax[1].add_patch(rect_patch)\n",
+ "\n",
+ " add_rectangle_to_plot(feed_rect, facecolor=\"gold\", edgecolor=\"black\")\n",
+ "\n",
+ " legends_init_rect = [\n",
+ " \"initial\" if (idx == 0) else None for idx in range(0, len(init_rectangles))\n",
+ " ]\n",
+ " legends_final_rect = [\n",
+ " \"final\" if (idx == 0) else None for idx in range(0, len(final_rectangles))\n",
+ " ]\n",
+ "\n",
+ " # plot rectangles from the second list (gold)\n",
+ " for idx, rect_obj in enumerate(final_rectangles):\n",
+ " add_rectangle_to_plot(\n",
+ " rect_obj, facecolor=\"gold\", edgecolor=\"black\", legend=legends_final_rect[idx]\n",
+ " ) # Added black edge for consistency\n",
+ "\n",
+ " # plot rectangles from the first list (gray, 0.25 alpha, dotted black border)\n",
+ " for idx, rect_obj in enumerate(init_rectangles):\n",
+ " add_rectangle_to_plot(\n",
+ " rect_obj,\n",
+ " facecolor=\"gray\",\n",
+ " alpha=0.25,\n",
+ " edgecolor=\"black\",\n",
+ " linestyle=\"dotted\",\n",
+ " legend=legends_init_rect[idx],\n",
+ " )\n",
+ "\n",
+ " # calculate plot limits\n",
+ " if all_rects_params:\n",
+ " min_x = min(p[0] for p in all_rects_params)\n",
+ " min_y = min(p[1] for p in all_rects_params)\n",
+ " max_x = max(p[0] + p[2] for p in all_rects_params) # max x is bottom_left_x + width\n",
+ " max_y = max(p[1] + p[3] for p in all_rects_params) # max y is bottom_left_y + height\n",
+ "\n",
+ " # add some padding to the limits\n",
+ " padding_x = (max_x - min_x) * 0.1 if (max_x - min_x) > 0 else 1\n",
+ " padding_y = (max_y - min_y) * 0.1 if (max_y - min_y) > 0 else 1\n",
+ "\n",
+ " ax[1].set_xlim(min_x - padding_x, max_x + padding_x)\n",
+ " ax[1].set_ylim(min_y - padding_y, max_y + padding_y)\n",
+ " else:\n",
+ " # default limits if no rectangles are plotted\n",
+ " ax[1].set_xlim(-5, 5)\n",
+ " ax[1].set_ylim(-5, 5)\n",
+ "\n",
+ " ax[1].set_title(\"Antenna Geometry\")\n",
+ " ax[1].set_xlabel(\"X-coordinate (mm)\")\n",
+ " ax[1].set_ylabel(\"Y-coordinate (mm)\")\n",
+ " ax[1].grid(True, linestyle=\"--\", alpha=0.7)\n",
+ "\n",
+ " ax[1].legend(loc=\"lower left\", bbox_to_anchor=(1.0, 0.75))\n",
+ "\n",
+ " ax[0].plot(figure_of_merit, color=\"green\", linewidth=2)\n",
+ " ax[0].set_title(\"Optimization\")\n",
+ " ax[0].set_xlabel(\"Iteration\")\n",
+ " ax[0].set_ylabel(\"Figure of Merit\")\n",
+ "\n",
+ " if savefig_fname:\n",
+ " plt.savefig(savefig_fname)\n",
+ "\n",
+ " plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Demonstrating Gradient Based Optimization of a Patch Antenna\n",
+ "\n",
+ "In our optimization, we use the adjoint gradients to tune the width and height of a single patch antenna element. As a first step, we create an objective function that takes in parameters defining the antenna geometry which correspond to `Box` geometries that can be imported into the simulation. After adding the source via the `TerminalComponentModeler`, the objective runs a simulation and we compute the `S11` spectrum and flux into a band of angles above the antenna. `S11` is a measure of how much energy is reflected and the radiated flux over a set of angles gives us a good figure of merit for directivity. We optimize the antenna to direct the radiated power at 0 degrees, directly outward from the antenna plane. The two figures are combined together into one by computing reflection efficiency as $1 - |S_{11}|^2$ and multiplying by the sum of the flux over a narrow band of angles around 0 degrees normalized by the initial flux in each of these angles, which is specified through `poynting_flux_initial`. The `objective_fn` uses the helper function `compute_poynting_and_s11` to compute these metrics.\n",
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "\n",
+ "Below, we show the functional flow for computing the objective function below and where automatic differentiation comes into the picture. By plugging into the `autograd` library with tidy3d, we can compute the gradient function for our objective function. In each iteration of the optimization, we use this function to calcualte the gradient of the objective with respect to the antenna patch parameters. Using this gradient, the parameters are updated to improve the objective function value. This process continues in our case for a fixed number of iterations but can also be done until convergence or some other criteria are met.\n",
+ "\n",
+ "
\n",
+ " \n",
+ "
\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def compute_poynting_and_s11(\n",
+ " patch_params,\n",
+ " log_simulation_cost=False,\n",
+ "):\n",
+ " \"\"\"\n",
+ " Computes the Poynting flux for an antenna. The antenna parameters are specified in and\n",
+ " `patch_params` are converted these parameters to simulation objects. These objects are inserted\n",
+ " into a base simulation. After setup, the antenna simulation is run to compute the `S11` and the\n",
+ " Poynting flux considering just the angular components in the `FieldProjectionAngleMonitor`.\n",
+ " \"\"\"\n",
+ " base_sim = create_base_sim()\n",
+ "\n",
+ " # set up monitor that will be used as a data source for far field projection\n",
+ " monitor_radiation = td.FieldMonitor(\n",
+ " center=[directivity_center_x, directivity_center_y, 0.5 * sub_z + 5 * mm],\n",
+ " size=(directivity_size_x, directivity_size_y, 0.0),\n",
+ " freqs=opt_freqs,\n",
+ " name=\"radiation\",\n",
+ " colocate=False,\n",
+ " )\n",
+ " # set up monitor that will be used to project radiation to the far field so we can\n",
+ " # optimize for directivity\n",
+ " monitor_far_field = td.FieldProjectionAngleMonitor(\n",
+ " center=[directivity_center_x, directivity_center_y, 0.5 * sub_z + 5 * mm],\n",
+ " size=(directivity_size_x, directivity_size_y, 0.0),\n",
+ " freqs=opt_freqs,\n",
+ " name=\"far_field\",\n",
+ " phi=phi_opt,\n",
+ " theta=theta_opt,\n",
+ " far_field_approx=True,\n",
+ " proj_distance=50 * wavelength0, # project far away form antenna\n",
+ " )\n",
+ "\n",
+ " antenna_monitors = [monitor_radiation, monitor_far_field]\n",
+ "\n",
+ " # Add antenna patches to simulation\n",
+ " sim_with_patches = create_simulation_with_patches(\n",
+ " base_sim, params_to_boxes(patch_params), antenna_monitors\n",
+ " )\n",
+ "\n",
+ " # Create the `TerminalComponentModeler` to add the source and get the simulation we can run\n",
+ " # to evaluate the antenna performance\n",
+ " modeler_freqs = sorted(list(set(list(opt_sim_freqs) + list(opt_freqs))))\n",
+ "\n",
+ " modeler = create_modeler(\n",
+ " sim_with_patches, modeler_freqs,\n",
+ " )\n",
+ "\n",
+ " # Run the simulations for the antenna\n",
+ " if log_simulation_cost:\n",
+ " job = td.web.Job(simulation=modeler, task_name=\"smatrix\", verbose=False)\n",
+ " smatrix_data = job.run()\n",
+ " cost = td.web.real_cost(job.task_id)\n",
+ " console.print(\n",
+ " f\"The antenna cost [bold yellow]{cost:.3f}[/bold yellow] FlexCredits to run. \"\n",
+ " f\"Each iteration of the optimization will cost around \"\n",
+ " f\"[bold cyan]{2*cost:.3f}[/bold cyan] FlexCredits to complete.\"\n",
+ " )\n",
+ " else:\n",
+ " smatrix_data = run(modeler, task_name=\"smatrix\", verbose=False)\n",
+ " \n",
+ " # Set up a `FieldProjector` based on the 'radiation' monitor near the patch\n",
+ " projector = td.FieldProjector.from_near_field_monitors(\n",
+ " sim_data=smatrix_data.data[\"lumped_port\"],\n",
+ " near_monitors=[monitor_radiation],\n",
+ " normal_dirs=[\"+\"], # we are projecting along the +z direction\n",
+ " )\n",
+ " # Project this near field into the subset of far field components\n",
+ " # specified by the 'far_field' monitor\n",
+ " radiation_data = projector.project_fields(monitor_far_field)\n",
+ " poynting_flux = np.abs(\n",
+ " np.real(\n",
+ " 0.5\n",
+ " * (\n",
+ " radiation_data.Etheta * np.conj(radiation_data.Hphi)\n",
+ " - radiation_data.Ephi * np.conj(radiation_data.Htheta)\n",
+ " )\n",
+ " )\n",
+ " )\n",
+ "\n",
+ " return poynting_flux, smatrix_data.smatrix()\n",
+ "\n",
+ "\n",
+ "def objective_fn(patch_params, poynting_flux_initial):\n",
+ " \"\"\"\n",
+ " Objective function for an antenna optimization that computes a product of the reflection\n",
+ " efficiency and the increased flux in a set of optimization angles compared to the initial design.\n",
+ " \"\"\"\n",
+ "\n",
+ " def weights_from_merit(merit):\n",
+ " \"\"\"\n",
+ " Computes performance based weights that sum to a total weight of 1.\n",
+ " For a given frequency, the weights are inversely tied to the performance\n",
+ " so that figures of merit that are lagging get favored more than those that\n",
+ " are leading.\n",
+ " \"\"\"\n",
+ " weights = (2.0 / len(merit)) - (merit**2 / anp.sum(merit**2))\n",
+ " clip_weights = anp.maximum(weights, 0.0)\n",
+ "\n",
+ " inv_weights = 1.0 / anp.sum(clip_weights)\n",
+ " renorm_weights = clip_weights * inv_weights\n",
+ "\n",
+ " return renorm_weights\n",
+ "\n",
+ " poynting_flux, smatrix = compute_poynting_and_s11(patch_params)\n",
+ "\n",
+ " fom_by_freq = []\n",
+ " for freq in opt_freqs:\n",
+ " directivity_metric = np.sum(poynting_flux.sel(f=freq).data) / np.sum(\n",
+ " poynting_flux_initial.sel(f=freq).data\n",
+ " )\n",
+ "\n",
+ " s11 = np.abs(smatrix.data.isel(port_out=0, port_in=0).sel(f=freq).data)\n",
+ "\n",
+ " fom = (1 - np.abs(s11) ** 2) * directivity_metric\n",
+ " fom_by_freq.append(fom)\n",
+ "\n",
+ " fom_by_freq = anp.array(fom_by_freq)\n",
+ "\n",
+ " if len(fom_by_freq) > 1:\n",
+ " weights = weights_from_merit(fom_by_freq) # dynamic optimization weights\n",
+ " return anp.sum(weights * fom_by_freq)\n",
+ " else:\n",
+ " return anp.sum(fom_by_freq)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We create a helper function to convert the patch width and height parameters to the antenna geometry for a single patch. To check our setup, we plot what the resulting antenna will look like when inserted into a simulation."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "def params_to_boxes(patch_params):\n",
+ " \"\"\"Convert single patch parameters into patch geometry.\"\"\"\n",
+ " patch_wh = patch_params[0:2]\n",
+ " main_patch = td.Box.from_bounds(\n",
+ " [-0.5 * patch_wh[0], 0.0, sub_z / 2.0], [0.5 * patch_wh[0], patch_wh[1], sub_z / 2.0]\n",
+ " )\n",
+ " return [main_patch]\n",
+ "\n",
+ "\n",
+ "# set an initial width and height for the patch\n",
+ "patch_init_width = 12.45 * mm\n",
+ "patch_init_height = 16 * mm\n",
+ "patch_init_wh = anp.array([patch_init_width, patch_init_height])\n",
+ "\n",
+ "# set the min/max for the patch width and height to use to bound these parameters in the optimization\n",
+ "patch_min_width = 8 * mm\n",
+ "patch_min_height = 8 * mm\n",
+ "\n",
+ "patch_max_width = 18 * mm\n",
+ "patch_max_height = 24 * mm\n",
+ "\n",
+ "# plot the patch and mesh of the initial structure to visually inspect geometry before starting optimization\n",
+ "plot_structures_and_mesh(params_to_boxes(patch_init_wh))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now, we set up the optimization. We create the monitors to capture the far field radiation and solve for the initial flux into the target radiation angular components. Then, we configure the optimizer which we can also load from a saved checkpoint by toggling the `restart_optimization` flag if we get interrupted in the middle. When computing the initial values of `S11` and the radiated flux, we log the simulation cost to give an idea of how many FlexCredits it costs run. During the optimization loop, each iteration we compute the gradient, which requires two simulations. The cost per iteration will thus be double that of a single simulation except for the occasional iteration where we save additional simulation data for evaluation after the optimization. On those iterations, the cost will be 3x the single simulation cost. To reduce the number of the full evaluations, you can increase `full_eval_period`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "
11:17:31 EDT Component modeler batch validation has been successful. \n",
+ "
11:18:11 EDT Billed FlexCredit cost: 0.025. Minimum cost depends on task \n",
+ "execution details. Use 'web.real_cost(task_id)' to get the billed \n",
+ "FlexCredit cost after a simulation run. \n",
+ "
\n"
+ ],
+ "text/plain": [
+ "\u001b[2;36m11:18:11 EDT\u001b[0m\u001b[2;36m \u001b[0mBilled FlexCredit cost: \u001b[1;36m0.025\u001b[0m. Minimum cost depends on task \n",
+ "\u001b[2;36m \u001b[0mexecution details. Use \u001b[32m'web.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m to get the billed \n",
+ "\u001b[2;36m \u001b[0mFlexCredit cost after a simulation run. \n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "
The antenna cost 0.03 FlexCredits to run. Each iteration of the optimization will cost around 0.05 FlexCredits to \n",
+ "complete.\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "The antenna cost \u001b[1;33m0.03\u001b[0m FlexCredits to run. Each iteration of the optimization will cost around \u001b[1;36m0.05\u001b[0m FlexCredits to \n",
+ "complete.\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Turn the logging level to only show errors to not crowd the optimization output with repeated warnings\n",
+ "td.config.logging_level = \"ERROR\"\n",
+ "\n",
+ "# whether or not to restart the optimization from the current saved checkpoint\n",
+ "restart_optimization = False\n",
+ "restart_fname = \"misc/rf_autograd_1_patch_opt_state.pkl\"\n",
+ "\n",
+ "# set up the single patch radiation monitor sizes\n",
+ "obj_fn_monitor_dim = 1.25 * np.mean(patch_init_wh)\n",
+ "obj_fn_monitor_center_y = 0.5 * patch_init_wh[1]\n",
+ "\n",
+ "# compute initial poynting flux to be used in the objective function\n",
+ "initial_poynting_flux, initial_s11 = compute_poynting_and_s11(patch_init_wh, log_simulation_cost=True)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Finally, we run the optimization and capture the figure of merit, `S11`, and gain metrics along the way."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "
![](\"img/PatchAntenna.png\")
\n",
+ " ![](\"img/single_patch_antenna.png\")
\n",
+ " ![](\"img/multi_patch_antenna.png\")
\n",
+ "![](\"img/antenna_figure_of_merit.png\")
"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def compute_poynting_and_s11(\n",
+ " patch_params,\n",
+ " patch_params_to_boxes,\n",
+ " sub_x_bounds,\n",
+ " sub_y_bounds,\n",
+ " optimization_monitors,\n",
+ " opt_freqs,\n",
+ "):\n",
+ " \"\"\"\n",
+ " Computes the Poynting flux for an antenna. The antenna parameters are specified in and\n",
+ " `patch_params` and a function `patch_params_to_boxes` convert these parameters into\n",
+ " simulation objects. These objects are inserted into a base simulation defined by\n",
+ " `sub_x_bounds` and `sub_y_bounds`. After setting up the antenna simulation including\n",
+ " the `optimization_monitors`, the far fields are computed\n",
+ " are,\n",
+ " the antenna simulation is run with the Poynting flux being computed with angular components\n",
+ "\n",
+ " \"\"\"\n",
+ " base_sim = create_base_sim(sub_x_bounds, sub_y_bounds)\n",
+ "\n",
+ " directivity_monitors = [\n",
+ " monitor for monitor in optimization_monitors if isinstance(monitor, td.DirectivityMonitor)\n",
+ " ]\n",
+ " non_directivity_monitors = [\n",
+ " monitor\n",
+ " for monitor in optimization_monitors\n",
+ " if not isinstance(monitor, td.DirectivityMonitor)\n",
+ " ]\n",
+ "\n",
+ " # Add antenna patches to simulation\n",
+ " sim_with_patches = create_simulation_with_patches(\n",
+ " base_sim, patch_params_to_boxes(patch_params), non_directivity_monitors\n",
+ " )\n",
+ "\n",
+ " # Create the `TerminalComponentModeler` to add the source and get the simulation we can run\n",
+ " # to evaluate the antenna performance\n",
+ " modeler_freqs = sorted(list(set(list(opt_sim_freqs) + list(opt_freqs))))\n",
+ "\n",
+ " modeler = create_modeler(\n",
+ " sim_with_patches, modeler_freqs, radiation_monitors=directivity_monitors\n",
+ " )\n",
+ "\n",
+ " # Run the simulations for the antenna.\n",
+ " smatrix_data = run(modeler, task_name=\"smatrix\", verbose=False)\n",
+ "\n",
+ " radiation_monitors = [m for m in optimization_monitors if m.name == \"radiation\"]\n",
+ " far_field_monitor = [m for m in optimization_monitors if m.name == \"far_field\"][0]\n",
+ " # Set up a `FieldProjector` based on the 'radiation' monitor near the patch\n",
+ " projector = td.FieldProjector.from_near_field_monitors(\n",
+ " sim_data=smatrix_data.data[\"lumped_port\"],\n",
+ " near_monitors=radiation_monitors,\n",
+ " normal_dirs=[\"+\"], # we are projecting along the +z direction\n",
+ " )\n",
+ " # Project this near field into the subset of far field components\n",
+ " # specified by the 'far_field' monitor\n",
+ " radiation_data = projector.project_fields(far_field_monitor)\n",
+ " poynting_flux = np.abs(\n",
+ " np.real(\n",
+ " 0.5\n",
+ " * (\n",
+ " radiation_data.Etheta * np.conj(radiation_data.Hphi)\n",
+ " - radiation_data.Ephi * np.conj(radiation_data.Htheta)\n",
+ " )\n",
+ " )\n",
+ " )\n",
+ "\n",
+ " return poynting_flux, smatrix_data.smatrix()\n",
+ "\n",
+ "\n",
+ "def objective_fn(\n",
+ " patch_params,\n",
+ " patch_params_to_boxes,\n",
+ " sub_x_bounds,\n",
+ " sub_y_bounds,\n",
+ " optimization_monitors,\n",
+ " opt_freqs,\n",
+ " poynting_flux_initial,\n",
+ "):\n",
+ " \"\"\"\n",
+ " Objective function for an antenna optimization that computes a product of the reflection\n",
+ " efficiency and the increased flux in a set of optimization angles compared to the initial design.\n",
+ " \"\"\"\n",
+ "\n",
+ " def weights_from_merit(merit):\n",
+ " \"\"\"\n",
+ " Computes performance based weights that sum to a total weight of 1.\n",
+ " For a given frequency, the weights are inversely tied to the performance\n",
+ " so that figures of merit that are lagging get favored more than those that\n",
+ " are leading.\n",
+ " \"\"\"\n",
+ "\n",
+ " weights = (2.0 / len(merit)) - (merit**2 / anp.sum(merit**2))\n",
+ " clip_weights = anp.maximum(weights, 0.0)\n",
+ "\n",
+ " inv_weights = 1.0 / anp.sum(clip_weights)\n",
+ " renorm_weights = clip_weights * inv_weights\n",
+ "\n",
+ " return renorm_weights\n",
+ "\n",
+ " poynting_flux, smatrix = compute_poynting_and_s11(\n",
+ " patch_params,\n",
+ " patch_params_to_boxes,\n",
+ " sub_x_bounds,\n",
+ " sub_y_bounds,\n",
+ " optimization_monitors,\n",
+ " opt_freqs,\n",
+ " )\n",
+ "\n",
+ " fom_by_freq = []\n",
+ " for freq in opt_freqs:\n",
+ " directivity_metric = np.sum(poynting_flux.sel(f=freq).data) / np.sum(\n",
+ " poynting_flux_initial.sel(f=freq).data\n",
+ " )\n",
+ "\n",
+ " s11 = np.abs(smatrix.data.isel(port_out=0, port_in=0).sel(f=freq).data)\n",
+ "\n",
+ " fom = (1 - np.abs(s11) ** 2) * directivity_metric\n",
+ " fom_by_freq.append(fom)\n",
+ "\n",
+ " fom_by_freq = anp.array(fom_by_freq)\n",
+ "\n",
+ " if len(fom_by_freq) > 1:\n",
+ " weights = weights_from_merit(fom_by_freq) # dynamic optimization weights\n",
+ " return anp.sum(weights * fom_by_freq)\n",
+ " else:\n",
+ " return anp.sum(fom_by_freq)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We then create a helper function to convert the patch width and height parameters to the antenna geometry for a single patch. To check out setup, we plot what the resulting antenna will look like when inserted into a simulation."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "![](\"img/pec_optimization_workflow.png\")
\n",
+ "