{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Inverse design optimization of a compact grating coupler\n",
        "\n",
        "> Note: Tidy3D now supports automatic differentiation natively through `autograd`. The `jax`-based `adjoint` plugin will be deprecated from 2.7 onwards. To see this notebook implemented in the new feature, see [this notebook](https://www.flexcompute.com/tidy3d/examples/notebooks/Autograd6GratingCoupler/).\n",
        "\n",
        "> To install the `jax` module required for this feature, we recommend running `pip install \"tidy3d[jax]\"`.\n",
        "\n",
        "**This notebook contains a long optimization. Running the entire notebook will cost about 10 FlexCredits and take a few hours.**\n",
        "\n",
        "The ability to couple light in and out of photonic integrated circuits (PICs) is crucial for developing wafer-scale systems and tests. This need makes designing efficient and compact grating couplers an important task in the PIC development cycle. In this notebook, we will demonstrate how to use [Tidy3D's adjoint plugin](../notebooks/AdjointPlugin1Intro.html) to perform the inverse design of a compact 3D grating coupler. We will show how to improve design fabricability by enhancing permittivity binarization and controlling the device's minimum feature size.\n",
        "\n",
        "<img src=\"img/adjoint_6.png\" width=400 alt=\"Schematic of the grating coupler\">\n",
        "\n",
        "In addition, if you are interested in more conventional designs, we modeled an [uniform grating coupler](https://www.flexcompute.com/tidy3d/examples/notebooks/GratingCoupler/) and a [Focusing apodized grating coupler](https://www.flexcompute.com/tidy3d/examples/notebooks/FocusedApodGC/) in previous case studies. For more integrated photonic examples, please visit our [examples page](https://www.flexcompute.com/tidy3d/examples/). If you are new to the finite-difference time-domain (FDTD) method, we highly recommend going through our [FDTD101](https://www.flexcompute.com/fdtd101/) tutorials. FDTD simulations can diverge due to various reasons. If you run into any simulation divergence issues, please follow the steps outlined in our [troubleshooting guide](https://www.flexcompute.com/tidy3d/examples/notebooks/DivergedFDTDSimulation/) to resolve it.\n",
        "\n",
        "We start by importing our typical python packages, `jax`, `tidy3d` and its `adjoint` plugin."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Standard python imports.\n",
        "import json\n",
        "import os\n",
        "from typing import Callable, List\n",
        "\n",
        "# Import jax to be able to use automatic differentiation.\n",
        "import jax.numpy as jnp\n",
        "import jax.scipy as jsp\n",
        "import matplotlib.pylab as plt\n",
        "import numpy as np\n",
        "import pydantic as pd\n",
        "import scipy as sp\n",
        "\n",
        "# Import regular tidy3d.\n",
        "import tidy3d as td\n",
        "import tidy3d.web as web\n",
        "from jax import value_and_grad\n",
        "\n",
        "# Import the components we need from the adjoint plugin.\n",
        "from tidy3d.plugins.adjoint import (\n",
        "    JaxBox,\n",
        "    JaxCustomMedium,\n",
        "    JaxDataArray,\n",
        "    JaxPermittivityDataset,\n",
        "    JaxSimulation,\n",
        "    JaxSimulationData,\n",
        "    JaxStructure,\n",
        ")\n",
        "from tidy3d.plugins.adjoint.utils.penalty import ErosionDilationPenalty\n",
        "from tidy3d.plugins.adjoint.web import run"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Grating Coupler Inverse Design Configuration\n",
        "\n",
        "The grating coupler inverse design begins with a rectangular design region connected to a $Si$ waveguide. Throughout the optimization process, this initial structure evolves to convert a vertically incident Gaussian-like mode from an optical fiber into a guided mode and then funnel it into the $Si$ waveguide.\n",
        "\n",
        "We are considering a full-etched grating structure, so a $SiO_{2}$ BOX layer is included. To reduce backreflection, we adjusted the fiber tilt angle to $10^{\\circ}$ [[1](https://doi.org/10.1364/OE.23.022628), [2](https://doi.org/10.3390/mi11070666)].\n",
        "\n",
        "In the following block of code, you can find the parameters that can be modified to configure the grating coupler structure, optimization, and simulation setup. Special care should be devoted to the `it_per_step` and `opt_steps` variables below."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Geometric parameters.\n",
        "w_thick = 0.22  # Waveguide thickness (um).\n",
        "w_width = 0.5  # Waveguide width (um).\n",
        "w_length = 1.0  # Waveguide length (um).\n",
        "box_thick = 1.6  # SiO2 BOX thickness (um).\n",
        "spot_size = 2.5  # Spot size of the input Gaussian field regarding a lensed fiber (um).\n",
        "fiber_tilt = 10.0  # Fiber tilt angle (degrees).\n",
        "src_offset = 0.05  # Distance between the source focus and device (um).\n",
        "\n",
        "# Material.\n",
        "nSi = 3.48  # Silicon refractive index.\n",
        "nSiO2 = 1.44  # Silica refractive index.\n",
        "\n",
        "# Design region parameters.\n",
        "gc_width = 4.0  # Grating coupler width (um).\n",
        "gc_length = 4.0  # Grating coupler length (um).\n",
        "dr_grid_size = 0.02  # Grid size within the design region (um).\n",
        "\n",
        "# Inverse design set up parameters.\n",
        "#################################################################\n",
        "# Total number of iterations = opt_steps x it_per_step.\n",
        "it_per_step = 1  # Number of iterations per optimization step.\n",
        "opt_steps = 75  # Number of optimization steps.\n",
        "#################################################################\n",
        "eta = 0.50  # Threshold value for the projection filter.\n",
        "fom_name = \"fom_field\"  # Name of the monitor used to compute the objective function.\n",
        "\n",
        "# Simulation wavelength.\n",
        "wl = 1.55  # Central simulation wavelength (um).\n",
        "bw = 0.06  # Simulation bandwidth (um).\n",
        "n_wl = 61  # Number of wavelength points within the bandwidth.\n",
        "\n",
        "# feature size\n",
        "min_feature_size = 0.080\n",
        "filter_radius = min_feature_size\n",
        "\n",
        "# Buffer layer thickness\n",
        "border_buffer = 0.16\n",
        "\n",
        "# projection\n",
        "beta_min = 1.0\n",
        "beta_max = 30.0"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Total iterations = 75\n"
          ]
        }
      ],
      "source": [
        "total_iter = opt_steps * it_per_step\n",
        "print(f\"Total iterations = {total_iter}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Inverse Design Optimization Set Up\n",
        "\n",
        "We will calculate the values of some parameters used throughout the inverse design set up."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Minimum and maximum values for the permittivities.\n",
        "eps_max = nSi**2\n",
        "eps_min = 1.0\n",
        "\n",
        "# Material definitions.\n",
        "mat_si = td.Medium(permittivity=eps_max)  # Waveguide material.\n",
        "mat_sio2 = td.Medium(permittivity=nSiO2**2)  # Substrate material.\n",
        "\n",
        "# Wavelengths and frequencies.\n",
        "wl_max = wl + bw / 2\n",
        "wl_min = wl - bw / 2\n",
        "wl_range = np.linspace(wl_min, wl_max, n_wl)\n",
        "freq = td.C_0 / wl\n",
        "freqs = td.C_0 / wl_range\n",
        "freqw = 0.5 * (freqs[0] - freqs[-1])\n",
        "run_time = 5e-12\n",
        "\n",
        "# Computational domain size.\n",
        "pml_spacing = 0.6 * wl\n",
        "size_x = pml_spacing + w_length + gc_length + 2 * border_buffer\n",
        "size_y = gc_width + 2 * pml_spacing + 2 * border_buffer\n",
        "size_z = w_thick + box_thick + 2 * pml_spacing\n",
        "center_z = size_z / 2 - pml_spacing - w_thick / 2\n",
        "eff_inf = 1000\n",
        "\n",
        "# Inverse design variables.\n",
        "src_pos_z = w_thick / 2 + src_offset\n",
        "mon_pos_x = -size_x / 2 + 0.25 * wl\n",
        "mon_w = int(3 * w_width / dr_grid_size) * dr_grid_size\n",
        "mon_h = int(5 * w_thick / dr_grid_size) * dr_grid_size\n",
        "nx = int((gc_length + 2 * border_buffer) / dr_grid_size)\n",
        "ny = int((gc_width + 2 * border_buffer) / dr_grid_size / 2.0)\n",
        "npar = int(nx * ny)\n",
        "dr_size_x = nx * dr_grid_size\n",
        "dr_size_y = 2 * ny * dr_grid_size\n",
        "dr_center_x = -size_x / 2 + w_length + dr_size_x / 2\n",
        "n_border = int(border_buffer / dr_grid_size)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "First, we will introduce the simulation components that do not change during optimization, such as the $Si$ waveguide and $SiO_{2}$ BOX layer. Additionally, we will include a Gaussian source to drive the simulations, and a mode monitor to compute the objective function."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Input/output waveguide.\n",
        "waveguide = td.Structure(\n",
        "    geometry=td.Box.from_bounds(\n",
        "        rmin=(-eff_inf, -w_width / 2, -w_thick / 2),\n",
        "        rmax=(-size_x / 2 + w_length, w_width / 2, w_thick / 2),\n",
        "    ),\n",
        "    medium=mat_si,\n",
        ")\n",
        "\n",
        "# SiO2 BOX layer.\n",
        "sio2_substrate = td.Structure(\n",
        "    geometry=td.Box.from_bounds(\n",
        "        rmin=(-eff_inf, -eff_inf, -w_thick / 2 - box_thick),\n",
        "        rmax=(eff_inf, eff_inf, -w_thick / 2),\n",
        "    ),\n",
        "    medium=mat_sio2,\n",
        ")\n",
        "\n",
        "# Si substrate.\n",
        "si_substrate = td.Structure(\n",
        "    geometry=td.Box.from_bounds(\n",
        "        rmin=(-eff_inf, -eff_inf, -eff_inf),\n",
        "        rmax=(eff_inf, eff_inf, -w_thick / 2 - box_thick),\n",
        "    ),\n",
        "    medium=mat_si,\n",
        ")\n",
        "\n",
        "# Gaussian source focused above the grating coupler.\n",
        "gauss_source = td.GaussianBeam(\n",
        "    center=(dr_center_x, 0, src_pos_z),\n",
        "    size=(dr_size_x - 2 * border_buffer, dr_size_y - 2 * border_buffer, 0),\n",
        "    source_time=td.GaussianPulse(freq0=freq, fwidth=freqw),\n",
        "    pol_angle=np.pi / 2,\n",
        "    angle_theta=fiber_tilt * np.pi / 180.0,\n",
        "    direction=\"-\",\n",
        "    num_freqs=7,\n",
        "    waist_radius=spot_size / 2,\n",
        ")\n",
        "\n",
        "# Monitor where we will compute the objective function from.\n",
        "mode_spec = td.ModeSpec(num_modes=1, target_neff=nSi)\n",
        "fom_monitor = td.ModeMonitor(\n",
        "    center=[mon_pos_x, 0, 0],\n",
        "    size=[0, mon_w, mon_h],\n",
        "    freqs=[freq],\n",
        "    mode_spec=mode_spec,\n",
        "    name=fom_name,\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Now, we will define a random vector of initial design parameters or load a previously designed structure.\n",
        "\n",
        "> Note: if a previous optimization file is found, the optimizer will pick up where that left off instead."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {},
      "outputs": [],
      "source": [
        "init_par = np.random.uniform(0, 1, int(npar))\n",
        "init_par = sp.ndimage.gaussian_filter(init_par, 1)\n",
        "init_par = init_par.reshape((nx, ny))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Fabrication Constraints\n",
        "\n",
        "We will use `jax` to build functions that improve device fabricability. A classical conic density filter, which is popular in topology optimization problems, is used to enforce a minimum feature size specified by the `filter_radius` variable. Next, a hyperbolic tangent projection function is applied to eliminate grayscale and obtain a binarized permittivity pattern. The `beta` parameter controls the sharpness of the transition in the projection function, and for better results, this parameter should be gradually increased throughout the optimization process. Finally, the design parameters are transformed into permittivity values. For a detailed review of these methods, refer to [[3](https://doi.org/10.1007/s00419-015-1106-4)].\n",
        "\n",
        "We will also introduce a buffer layer around the design region to enhance fabricability at the interfaces. The permittivity is enforced to lower values within the buffer layer, except at the output waveguide connection where we want a smooth transition."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {},
      "outputs": [],
      "source": [
        "from tidy3d.plugins.adjoint.utils.filter import BinaryProjector, ConicFilter\n",
        "\n",
        "conic_filter = ConicFilter(radius=filter_radius, design_region_dl=dr_grid_size)\n",
        "\n",
        "\n",
        "def tanh_projection(x, beta, eta=0.5):\n",
        "    tanhbn = jnp.tanh(beta * eta)\n",
        "    num = tanhbn + jnp.tanh(beta * (x - eta))\n",
        "    den = tanhbn + jnp.tanh(beta * (1 - eta))\n",
        "    return num / den\n",
        "\n",
        "\n",
        "def filter_project(x, beta, eta=0.5):\n",
        "    x = conic_filter.evaluate(x)\n",
        "    return tanh_projection(x, beta=beta, eta=eta)\n",
        "\n",
        "\n",
        "def interface_buffer(params):\n",
        "    \"\"\"Introduce a buffer around design to enhance fabricability at the interfaces.\"\"\"\n",
        "    par = jnp.asarray(params)\n",
        "    par = par.at[0:n_border, :].set(0)\n",
        "    par = par.at[nx - n_border :, :].set(0)\n",
        "    par = par.at[:, ny - n_border :].set(0)\n",
        "    par = par.at[0:n_border, 0 : int((w_width / 2) / dr_grid_size) + 1].set(1)\n",
        "    return par\n",
        "\n",
        "\n",
        "def pre_process(params, beta):\n",
        "    \"\"\"Get the permittivity values (1, eps_wg) array as a function of the parameters (0,1)\"\"\"\n",
        "    params1 = interface_buffer(params)\n",
        "    params2 = filter_project(params1, beta=beta)\n",
        "    params3 = filter_project(params2, beta=beta)\n",
        "    return params3\n",
        "\n",
        "\n",
        "def get_eps_values(params, beta):\n",
        "    params = pre_process(params, beta=beta)\n",
        "    eps_values = eps_min + (eps_max - eps_min) * params\n",
        "    return eps_values"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {},
      "outputs": [],
      "source": [
        "def get_eps(design_param, beta: float = 1.00, binarize: bool = False) -> np.ndarray:\n",
        "    \"\"\"Returns the permittivities after applying a conic density filter on design parameters\n",
        "    to enforce fabrication constraints, followed by a binarization projection function\n",
        "    which reduces grayscale.\n",
        "    Parameters:\n",
        "        design_param: np.ndarray\n",
        "            Vector of design parameters.\n",
        "        beta: float = 1.0\n",
        "            Sharpness parameter for the projection filter.\n",
        "        binarize: bool = False\n",
        "            Enforce binarization.\n",
        "    Returns:\n",
        "        eps: np.ndarray\n",
        "            Permittivity vector.\n",
        "    \"\"\"\n",
        "\n",
        "    # Calculates the permittivities from the transformed design parameters.\n",
        "    eps = get_eps_values(design_param, beta=beta)\n",
        "    if binarize:\n",
        "        eps = jnp.where(eps < (eps_min + eps_max) / 2, eps_min, eps_max)\n",
        "    else:\n",
        "        eps = jnp.where(eps < eps_min, eps_min, eps)\n",
        "        eps = jnp.where(eps > eps_max, eps_max, eps)\n",
        "    return eps"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "The permittivity values obtained from the design parameters are then used to build a [JaxCustomMedium](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.plugins.adjoint.JaxCustomMedium.html). As we will consider symmetry about the x-axis in the simulations, only the upper-half part of the design region needs to be populated. A `JaxStructure` built using the `JaxCustomMedium` will be returned by the following function: "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {},
      "outputs": [],
      "source": [
        "def update_design(eps, unfold: bool = False) -> List[JaxStructure]:\n",
        "    # Reflects the structure about the x-axis.\n",
        "    nyii = ny\n",
        "    y_min = 0\n",
        "    dr_s_y = dr_size_y / 2\n",
        "    dr_c_y = dr_s_y / 2\n",
        "    eps_val = jnp.array(eps).reshape((nx, ny, 1, 1))\n",
        "    if unfold:\n",
        "        nyii = 2 * ny\n",
        "        y_min = -dr_size_y / 2\n",
        "        dr_s_y = dr_size_y\n",
        "        dr_c_y = 0\n",
        "        eps_val = np.concatenate((np.fliplr(np.copy(eps_val)), eps_val), axis=1)\n",
        "\n",
        "    # Definition of the coordinates x,y along the design region.\n",
        "    coords_x = [(dr_center_x - dr_size_x / 2) + ix * dr_grid_size for ix in range(nx)]\n",
        "    coords_y = [y_min + iy * dr_grid_size for iy in range(nyii)]\n",
        "    coords = dict(x=coords_x, y=coords_y, z=[0], f=[freq])\n",
        "\n",
        "    # Creation of a custom medium using the values of the design parameters.\n",
        "    eps_components = {\n",
        "        f\"eps_{dim}{dim}\": JaxDataArray(values=eps_val, coords=coords) for dim in \"xyz\"\n",
        "    }\n",
        "    eps_dataset = JaxPermittivityDataset(**eps_components)\n",
        "    eps_medium = JaxCustomMedium(eps_dataset=eps_dataset)\n",
        "    box = JaxBox(center=(dr_center_x, dr_c_y, 0), size=(dr_size_x, dr_s_y, w_thick))\n",
        "    design_structure = JaxStructure(geometry=box, medium=eps_medium)\n",
        "    return [design_structure]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Next, we will write a function to return the `JaxSimulation` object. Note that we are using a `MeshOverrideStructure` to obtain a uniform mesh over the design region."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {},
      "outputs": [],
      "source": [
        "def make_adjoint_sim(\n",
        "    design_param, beta: float = 1.00, unfold: bool = False, binarize: bool = False\n",
        ") -> JaxSimulation:\n",
        "    # Builds the design region from the design parameters.\n",
        "    eps = get_eps(design_param, beta, binarize)\n",
        "    design_structure = update_design(eps, unfold=unfold)\n",
        "\n",
        "    # Creates a uniform mesh for the design region.\n",
        "    adjoint_dr_mesh = td.MeshOverrideStructure(\n",
        "        geometry=td.Box(center=(dr_center_x, 0, 0), size=(dr_size_x, dr_size_y, w_thick)),\n",
        "        dl=[dr_grid_size, dr_grid_size, dr_grid_size],\n",
        "        enforce=True,\n",
        "    )\n",
        "\n",
        "    return JaxSimulation(\n",
        "        size=[size_x, size_y, size_z],\n",
        "        center=[0, 0, -center_z],\n",
        "        grid_spec=td.GridSpec.auto(\n",
        "            wavelength=wl_max,\n",
        "            min_steps_per_wvl=15,\n",
        "            override_structures=[adjoint_dr_mesh],\n",
        "        ),\n",
        "        symmetry=(0, -1, 0),\n",
        "        structures=[waveguide, sio2_substrate, si_substrate],\n",
        "        input_structures=design_structure,\n",
        "        sources=[gauss_source],\n",
        "        monitors=[],\n",
        "        output_monitors=[fom_monitor],\n",
        "        run_time=run_time,\n",
        "        subpixel=True,\n",
        "    )"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Let's visualize the simulation set up and verify if all the elements are in their correct places."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x1000 with 4 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "init_design = make_adjoint_sim(init_par, beta=beta_min)\n",
        "\n",
        "fig, (ax1, ax2) = plt.subplots(1, 2, tight_layout=True, figsize=(10, 10))\n",
        "init_design.plot_eps(z=0, ax=ax1)\n",
        "init_design.plot_eps(y=0, ax=ax2)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Optimization\n",
        "\n",
        "We need to provide an objective function and its gradients with respect to the design parameters of the optimization algorithm.\n",
        "\n",
        "Our figure-of-merit (FOM) is the coupling efficiency of the incident power into the fundamental transverse electric mode of the $Si$ waveguide. The optimization algorithm will call the objective function at each iteration step. Therefore, the objective function will create the adjoint simulation, run it, and return the FOM value."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Figure of Merit (FOM) calculation.\n",
        "def fom(sim_data: JaxSimulationData) -> float:\n",
        "    \"\"\"Return the power at the mode index of interest.\"\"\"\n",
        "    output_amps = sim_data.output_data[0].amps\n",
        "    amp = output_amps.sel(direction=\"-\", f=freq, mode_index=0)\n",
        "    return jnp.sum(jnp.abs(amp) ** 2)\n",
        "\n",
        "\n",
        "def penalty(params, beta) -> float:\n",
        "    \"\"\"Penalty function based on amount of change in parameters after erosion and dilation.\"\"\"\n",
        "    params_processed = pre_process(params, beta=beta)\n",
        "    ed_penalty = ErosionDilationPenalty(length_scale=filter_radius, pixel_size=dr_grid_size)\n",
        "    return ed_penalty.evaluate(params_processed)\n",
        "\n",
        "\n",
        "# Objective function to be passed to the optimization algorithm.\n",
        "def obj(design_param, beta: float = 1.0, step_num: int = None, verbose: bool = False) -> float:\n",
        "    sim = make_adjoint_sim(design_param, beta)\n",
        "    task_name = \"inv_des\"\n",
        "    if step_num:\n",
        "        task_name += f\"_step_{step_num}\"\n",
        "    sim_data = run(sim, task_name=task_name, verbose=verbose)\n",
        "    fom_val = fom(sim_data)\n",
        "    feature_size_penalty = penalty(design_param, beta=beta)\n",
        "    J = fom_val - feature_size_penalty\n",
        "    return J, sim_data\n",
        "\n",
        "\n",
        "# Function to calculate the objective function value and its\n",
        "# gradient with respect to the design parameters.\n",
        "obj_grad = value_and_grad(obj, has_aux=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Next we will define the optimizer using `optax`. We will save the optimization progress in a `pickle` file. If that file is found, it will pick up the optimization from the last state. Otherwise, we will create a blank history."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "tags": []
      },
      "outputs": [],
      "source": [
        "import pickle\n",
        "\n",
        "import optax\n",
        "\n",
        "# hyperparameters\n",
        "learning_rate = 0.3\n",
        "optimizer = optax.adam(learning_rate=learning_rate)\n",
        "\n",
        "# where to store history\n",
        "history_fname = \"misc/grating_coupler_history.pkl\"\n",
        "\n",
        "\n",
        "def save_history(history_dict: dict) -> None:\n",
        "    \"\"\"Convenience function to save the history to file.\"\"\"\n",
        "    with open(history_fname, \"wb\") as file:\n",
        "        pickle.dump(history_dict, file)\n",
        "\n",
        "\n",
        "def load_history() -> dict:\n",
        "    \"\"\"Convenience method to load the history from file.\"\"\"\n",
        "    with open(history_fname, \"rb\") as file:\n",
        "        history_dict = pickle.load(file)\n",
        "    return history_dict"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "tags": []
      },
      "source": [
        "### Checking For a Previous Optimization\n",
        "\n",
        "If `history_fname` is a valid file, the results of a previous optimization are loaded, then the optimization will continue from the last iteration step. If the optimization was completed, only the final structure will be simulated. The pickle file used in this notebook can be downloaded from our documentation [repo](https://github.com/flexcompute/tidy3d-notebooks/tree/develop/misc)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "tags": []
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Loaded optimization checkpoint from file.\n",
            "Found 75 iterations previously completed out of 75 total.\n",
            "Optimization completed, will return results.\n"
          ]
        }
      ],
      "source": [
        "try:\n",
        "    history_dict = load_history()\n",
        "    opt_state = history_dict[\"opt_states\"][-1]\n",
        "    params = history_dict[\"params\"][-1]\n",
        "    num_iters_completed = len(history_dict[\"params\"])\n",
        "    print(\"Loaded optimization checkpoint from file.\")\n",
        "    print(f\"Found {num_iters_completed} iterations previously completed out of {total_iter} total.\")\n",
        "    if num_iters_completed < total_iter:\n",
        "        print(\"Will resume optimization.\")\n",
        "    else:\n",
        "        print(\"Optimization completed, will return results.\")\n",
        "\n",
        "except FileNotFoundError:\n",
        "    params = np.array(init_par)\n",
        "    opt_state = optimizer.init(params)\n",
        "    history_dict = dict(\n",
        "        values=[],\n",
        "        params=[],\n",
        "        gradients=[],\n",
        "        opt_states=[opt_state],\n",
        "        data=[],\n",
        "        beta=[],\n",
        "    )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "tags": []
      },
      "outputs": [],
      "source": [
        "iter_done = len(history_dict[\"values\"])\n",
        "\n",
        "for i in range(iter_done, total_iter):\n",
        "    print(f\"iteration = ({i + 1} / {total_iter})\")\n",
        "\n",
        "    # compute gradient and current objective function value\n",
        "    perc_done = i / (total_iter - 1)\n",
        "    beta_i = beta_min * (1 - perc_done) + beta_max * perc_done\n",
        "    (value, sim_data_i), gradient = obj_grad(params, beta=beta_i)\n",
        "\n",
        "    # outputs\n",
        "    print(f\"\\tbeta = {beta_i}\")\n",
        "    print(f\"\\tJ = {value:.4e}\")\n",
        "    print(f\"\\tgrad_norm = {np.linalg.norm(gradient):.4e}\")\n",
        "\n",
        "    # compute and apply updates to the optimizer based on gradient (-1 sign to maximize obj_fn)\n",
        "    updates, opt_state = optimizer.update(-gradient, opt_state, params)\n",
        "    params = optax.apply_updates(params, updates)\n",
        "\n",
        "    # cap parameters between 0 and 1\n",
        "    params = jnp.minimum(params, 1.0)\n",
        "    params = jnp.maximum(params, 0.0)\n",
        "\n",
        "    # save history\n",
        "    history_dict[\"values\"].append(value)\n",
        "    history_dict[\"params\"].append(params)\n",
        "    history_dict[\"beta\"].append(beta_i)\n",
        "    history_dict[\"gradients\"].append(gradient)\n",
        "    history_dict[\"opt_states\"].append(opt_state)\n",
        "    # history_dict[\"data\"].append(sim_data_i) # uncomment to store data, can create large files\n",
        "    save_history(history_dict)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Optimization Results\n",
        "\n",
        "After 150 iterations, a coupling efficiency value of 0.71 (-1.48 dB) was achieved at the central wavelength."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {},
      "outputs": [],
      "source": [
        "obj_vals = np.array(history_dict[\"values\"])\n",
        "final_par = history_dict[\"params\"][-1]\n",
        "final_beta = history_dict[\"beta\"][-1]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 600x400 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "fig, ax = plt.subplots(1, 1, figsize=(6, 4))\n",
        "ax.plot(obj_vals, \"ro\")\n",
        "ax.set_xlabel(\"iterations\")\n",
        "ax.set_ylabel(\"objective function\")\n",
        "ax.set_ylim(-1, 1)\n",
        "ax.set_title(f\"Final Objective Function Value: {obj_vals[-1]:.2f}\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "The final grating coupler structure is well binarized, with mostly black (`eps_max`) and white (`eps_min`) regions."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 400x400 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "fig, ax = plt.subplots(1, figsize=(4, 4))\n",
        "sim_final = make_adjoint_sim(final_par, beta=final_beta, unfold=True)\n",
        "sim_final = sim_final.to_simulation()[0]\n",
        "sim_final.plot_eps(z=0, source_alpha=0, monitor_alpha=0, ax=ax)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Once the inverse design is complete, we can visualize the field distributions and the wavelength dependent coupling efficiency."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">14:03:22 EDT </span>Created task <span style=\"color: #008000; text-decoration-color: #008000\">'inv_des_final'</span> with task_id                          \n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'fdve-ef15ddf7-f3f8-4925-8752-d687f37fba09'</span> and task_type <span style=\"color: #008000; text-decoration-color: #008000\">'FDTD'</span>.  \n",
              "</pre>\n"
            ],
            "text/plain": [
              "\u001b[2;36m14:03:22 EDT\u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'inv_des_final'\u001b[0m with task_id                          \n",
              "\u001b[2;36m             \u001b[0m\u001b[32m'fdve-ef15ddf7-f3f8-4925-8752-d687f37fba09'\u001b[0m and task_type \u001b[32m'FDTD'\u001b[0m.  \n"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>View task using web UI at                                          \n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-ef15ddf7-f3f8-4925-8752-d687f37fba09\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'https://tidy3d.simulation.cloud/workbench?taskId=fdve-ef15ddf7-f3f</span></a>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-ef15ddf7-f3f8-4925-8752-d687f37fba09\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">8-4925-8752-d687f37fba09'</span></a>.                                         \n",
              "</pre>\n"
            ],
            "text/plain": [
              "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mView task using web UI at                                          \n",
              "\u001b[2;36m             \u001b[0m\u001b]8;id=161143;https://tidy3d.simulation.cloud/workbench?taskId=fdve-ef15ddf7-f3f8-4925-8752-d687f37fba09\u001b\\\u001b[32m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=666516;https://tidy3d.simulation.cloud/workbench?taskId=fdve-ef15ddf7-f3f8-4925-8752-d687f37fba09\u001b\\\u001b[32mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=161143;https://tidy3d.simulation.cloud/workbench?taskId=fdve-ef15ddf7-f3f8-4925-8752-d687f37fba09\u001b\\\u001b[32m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=631353;https://tidy3d.simulation.cloud/workbench?taskId=fdve-ef15ddf7-f3f8-4925-8752-d687f37fba09\u001b\\\u001b[32mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=161143;https://tidy3d.simulation.cloud/workbench?taskId=fdve-ef15ddf7-f3f8-4925-8752-d687f37fba09\u001b\\\u001b[32m-ef15ddf7-f3f\u001b[0m\u001b]8;;\u001b\\\n",
              "\u001b[2;36m             \u001b[0m\u001b]8;id=161143;https://tidy3d.simulation.cloud/workbench?taskId=fdve-ef15ddf7-f3f8-4925-8752-d687f37fba09\u001b\\\u001b[32m8-4925-8752-d687f37fba09'\u001b[0m\u001b]8;;\u001b\\.                                         \n"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/\n",
              "rich/live.py:231: UserWarning: install \"ipywidgets\" for Jupyter support\n",
              "  warnings.warn('install \"ipywidgets\" for Jupyter support')\n",
              "</pre>\n"
            ],
            "text/plain": [
              "/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/\n",
              "rich/live.py:231: UserWarning: install \"ipywidgets\" for Jupyter support\n",
              "  warnings.warn('install \"ipywidgets\" for Jupyter support')\n"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
            ],
            "text/plain": []
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
              "</pre>\n"
            ],
            "text/plain": [
              "\n"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">14:03:25 EDT </span>status = queued                                                    \n",
              "</pre>\n"
            ],
            "text/plain": [
              "\u001b[2;36m14:03:25 EDT\u001b[0m\u001b[2;36m \u001b[0mstatus = queued                                                    \n"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>To cancel the simulation, use <span style=\"color: #008000; text-decoration-color: #008000\">'web.abort(task_id)'</span> or              \n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'web.delete(task_id)'</span> or abort/delete the task in the web UI.      \n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Terminating the Python script will not stop the job running on the \n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>cloud.                                                             \n",
              "</pre>\n"
            ],
            "text/plain": [
              "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mTo cancel the simulation, use \u001b[32m'web.abort\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m or              \n",
              "\u001b[2;36m             \u001b[0m\u001b[32m'web.delete\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m or abort/delete the task in the web UI.      \n",
              "\u001b[2;36m             \u001b[0mTerminating the Python script will not stop the job running on the \n",
              "\u001b[2;36m             \u001b[0mcloud.                                                             \n"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">14:03:29 EDT </span>status = preprocess                                                \n",
              "</pre>\n"
            ],
            "text/plain": [
              "\u001b[2;36m14:03:29 EDT\u001b[0m\u001b[2;36m \u001b[0mstatus = preprocess                                                \n"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
            ],
            "text/plain": []
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">14:03:32 EDT </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.213</span>. Use <span style=\"color: #008000; text-decoration-color: #008000\">'web.real_cost(task_id)'</span> to get\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>the billed FlexCredit cost after a simulation run.                 \n",
              "</pre>\n"
            ],
            "text/plain": [
              "\u001b[2;36m14:03:32 EDT\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m0.213\u001b[0m. 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\n",
              "\u001b[2;36m             \u001b[0mthe billed FlexCredit cost after a simulation run.                 \n"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>starting up solver                                                 \n",
              "</pre>\n"
            ],
            "text/plain": [
              "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mstarting up solver                                                 \n"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>running solver                                                     \n",
              "</pre>\n"
            ],
            "text/plain": [
              "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mrunning solver                                                     \n"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">14:03:58 EDT </span>early shutoff detected at <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">16</span>%, exiting.                            \n",
              "</pre>\n"
            ],
            "text/plain": [
              "\u001b[2;36m14:03:58 EDT\u001b[0m\u001b[2;36m \u001b[0mearly shutoff detected at \u001b[1;36m16\u001b[0m%, exiting.                            \n"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
            ],
            "text/plain": []
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
              "</pre>\n"
            ],
            "text/plain": [
              "\n"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>status = postprocess                                               \n",
              "</pre>\n"
            ],
            "text/plain": [
              "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mstatus = postprocess                                               \n"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">14:04:00 EDT </span>status = success                                                   \n",
              "</pre>\n"
            ],
            "text/plain": [
              "\u001b[2;36m14:04:00 EDT\u001b[0m\u001b[2;36m \u001b[0mstatus = success                                                   \n"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
            ],
            "text/plain": []
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>View simulation result at                                          \n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-ef15ddf7-f3f8-4925-8752-d687f37fba09\" target=\"_blank\"><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">'https://tidy3d.simulation.cloud/workbench?taskId=fdve-ef15ddf7-f3f</span></a>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-ef15ddf7-f3f8-4925-8752-d687f37fba09\" target=\"_blank\"><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">8-4925-8752-d687f37fba09'</span></a><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">.</span>                                         \n",
              "</pre>\n"
            ],
            "text/plain": [
              "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mView simulation result at                                          \n",
              "\u001b[2;36m             \u001b[0m\u001b]8;id=616720;https://tidy3d.simulation.cloud/workbench?taskId=fdve-ef15ddf7-f3f8-4925-8752-d687f37fba09\u001b\\\u001b[4;34m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=911319;https://tidy3d.simulation.cloud/workbench?taskId=fdve-ef15ddf7-f3f8-4925-8752-d687f37fba09\u001b\\\u001b[4;34mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=616720;https://tidy3d.simulation.cloud/workbench?taskId=fdve-ef15ddf7-f3f8-4925-8752-d687f37fba09\u001b\\\u001b[4;34m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=78268;https://tidy3d.simulation.cloud/workbench?taskId=fdve-ef15ddf7-f3f8-4925-8752-d687f37fba09\u001b\\\u001b[4;34mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=616720;https://tidy3d.simulation.cloud/workbench?taskId=fdve-ef15ddf7-f3f8-4925-8752-d687f37fba09\u001b\\\u001b[4;34m-ef15ddf7-f3f\u001b[0m\u001b]8;;\u001b\\\n",
              "\u001b[2;36m             \u001b[0m\u001b]8;id=616720;https://tidy3d.simulation.cloud/workbench?taskId=fdve-ef15ddf7-f3f8-4925-8752-d687f37fba09\u001b\\\u001b[4;34m8-4925-8752-d687f37fba09'\u001b[0m\u001b]8;;\u001b\\\u001b[4;34m.\u001b[0m                                         \n"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
            ],
            "text/plain": []
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
              "</pre>\n"
            ],
            "text/plain": [
              "\n"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">14:04:02 EDT </span>loading simulation from simulation_data.hdf5                       \n",
              "</pre>\n"
            ],
            "text/plain": [
              "\u001b[2;36m14:04:02 EDT\u001b[0m\u001b[2;36m \u001b[0mloading simulation from simulation_data.hdf5                       \n"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Field monitors to visualize the final fields.\n",
        "field_xy = td.FieldMonitor(\n",
        "    size=(td.inf, td.inf, 0),\n",
        "    freqs=[freq],\n",
        "    name=\"field_xy\",\n",
        ")\n",
        "\n",
        "field_xz = td.FieldMonitor(\n",
        "    size=(td.inf, 0, td.inf),\n",
        "    freqs=[freq],\n",
        "    name=\"field_xz\",\n",
        ")\n",
        "\n",
        "# Monitor to compute the grating coupler efficiency.\n",
        "gc_efficiency = td.ModeMonitor(\n",
        "    center=[mon_pos_x, 0, 0],\n",
        "    size=[0, mon_w, mon_h],\n",
        "    freqs=freqs,\n",
        "    mode_spec=mode_spec,\n",
        "    name=\"gc_efficiency\",\n",
        ")\n",
        "\n",
        "sim_final = sim_final.copy(update=dict(monitors=(field_xy, field_xz, gc_efficiency)))\n",
        "sim_data_final = web.run(sim_final, task_name=\"inv_des_final\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 800x600 with 6 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "mode_amps = sim_data_final[\"gc_efficiency\"]\n",
        "coeffs_f = mode_amps.amps.sel(direction=\"-\")\n",
        "power_0 = np.abs(coeffs_f.sel(mode_index=0)) ** 2\n",
        "power_0_db = 10 * np.log10(power_0)\n",
        "\n",
        "sim_plot = sim_final.updated_copy(symmetry=(0, 0, 0), monitors=(field_xy, field_xz, gc_efficiency))\n",
        "sim_data_plot = sim_data_final.updated_copy(simulation=sim_plot)\n",
        "\n",
        "f, ax = plt.subplots(2, 2, figsize=(8, 6), tight_layout=True)\n",
        "sim_plot.plot_eps(z=0, source_alpha=0, monitor_alpha=0, ax=ax[0, 1])\n",
        "ax[1, 0].plot(wl_range, power_0_db, \"-k\")\n",
        "ax[1, 0].set_xlabel(\"Wavelength (um)\")\n",
        "ax[1, 0].set_ylabel(\"Power (db)\")\n",
        "ax[1, 0].set_ylim(-15, 0)\n",
        "ax[1, 0].set_xlim(wl - bw / 2, wl + bw / 2)\n",
        "ax[1, 0].set_title(\"Coupling Efficiency\")\n",
        "sim_data_plot.plot_field(\"field_xy\", \"E\", \"abs^2\", z=0, ax=ax[1, 1])\n",
        "ax[0, 0].plot(obj_vals, \"ro\")\n",
        "ax[0, 0].set_xlabel(\"iterations\")\n",
        "ax[0, 0].set_ylabel(\"objective function\")\n",
        "ax[0, 0].set_ylim(-1, 1)\n",
        "ax[0, 0].set_title(f\"Final Objective Function Value: {obj_vals[-1]:.2f}\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "optimized loss of -2.38 dB\n"
          ]
        }
      ],
      "source": [
        "loss_db = max(power_0_db)\n",
        "print(f\"optimized loss of {loss_db:.2f} dB\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Export to GDS\n",
        "The `Simulation` object has the [.to_gds_file](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.Simulation.html#tidy3d.Simulation.to_gds_file) convenience function to export the final design to a `GDS` file. In addition to a file name, it is necessary to set a cross-sectional plane (`z = 0` in this case) on which to evaluate the geometry, a `frequency` to evaluate the permittivity, and a `permittivity_threshold` to define the shape boundaries in custom mediums. See the [GDS export](https://www.flexcompute.com/tidy3d/examples/notebooks/GDSExport/) notebook for a detailed example on using `.to_gds_file` and other GDS related functions."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {},
      "outputs": [],
      "source": [
        "sim_final.to_gds_file(\n",
        "    fname=\"./misc/inverse_designed_gc.gds\",\n",
        "    z=0,\n",
        "    permittivity_threshold=(eps_max + eps_min) / 2,\n",
        "    frequency=freq,\n",
        ")"
      ]
    }
  ],
  "metadata": {
    "description": "This notebook demonstrates the inverse design of a compact 3D grating coupler with permittivity binarization and minimum feature size control using the adjoint plugin.",
    "feature_image": "./img/adjoint_6.png",
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "keywords": "inverse design, grating coupler, photonic integrated circuits, design optimization, adjoint, Tidy3D, FDTD",
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.11.7"
    },
    "nbdime-conflicts": {
      "local_diff": [
        {
          "diff": [
            {
              "diff": [
                {
                  "diff": [
                    {
                      "key": 5,
                      "op": "addrange",
                      "valuelist": "9"
                    },
                    {
                      "key": 5,
                      "length": 1,
                      "op": "removerange"
                    }
                  ],
                  "key": 0,
                  "op": "patch"
                }
              ],
              "key": "version",
              "op": "patch"
            }
          ],
          "key": "language_info",
          "op": "patch"
        }
      ],
      "remote_diff": [
        {
          "diff": [
            {
              "diff": [
                {
                  "diff": [
                    {
                      "key": 5,
                      "op": "addrange",
                      "valuelist": "12"
                    },
                    {
                      "key": 5,
                      "length": 1,
                      "op": "removerange"
                    }
                  ],
                  "key": 0,
                  "op": "patch"
                }
              ],
              "key": "version",
              "op": "patch"
            }
          ],
          "key": "language_info",
          "op": "patch"
        }
      ]
    },
    "title": "Inverse Design of a Grating Coupler in Tidy3D Using the Adjoint Plugin | Flexcompute",
    "widgets": {
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