{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Inverse designed waveguide reflector and Fabry-Perot cavity\n",
        "\n",
        "**Note: the cost of running the entire notebook is larger than 10\n",
        "FlexCredits.**\n",
        "\n",
        "Compact on-chip mirrors are a key building block for integrated\n",
        "Fabry-Perot cavities, laser feedback sections, and sensing. A broadband,\n",
        "high-reflectivity mirror is difficult to realize with conventional Bragg\n",
        "gratings in a small footprint, which makes it an excellent target for\n",
        "adjoint-based topology optimization.\n",
        "\n",
        "In this notebook, we demonstrate the complete design flow of a\n",
        "terminating waveguide reflector on the 220 nm silicon-on-insulator\n",
        "platform with oxide cladding: a $1 \\times 3$ $\\mu m$ design region\n",
        "attached to a 500 nm single-mode waveguide is optimized to reflect the\n",
        "fundamental TE mode back into the waveguide, reaching about 99%\n",
        "reflectivity at 1550 nm while respecting a 100 nm minimum feature size\n",
        "in a fully binarized layout. We then assemble two copies of the mirror\n",
        "into a side-coupled Fabry-Perot cavity and compute its circuit-level\n",
        "spectrum, which shows a free spectral range of about 4.7 nm, deep\n",
        "on-resonance extinction, and a loaded quality factor of several\n",
        "thousand. Finally, we attach grating couplers following the SiEPIC\n",
        "layout conventions to produce a fabrication-ready chip layout.\n",
        "\n",
        "The workflow has six parts: [1](#1) robust inverse design of the\n",
        "reflector, [2](#2) binarization and minimum-feature-size enforcement,\n",
        "[3](#3) high-accuracy verification and GDS export, [4](#4) cavity\n",
        "circuit assembly and simulation with PhotonForge on the SiEPIC PDK,\n",
        "[5](#5) quality factor extraction, and [6](#6) the fabrication-ready\n",
        "layout with grating couplers.\n",
        "\n",
        "<img src=\"img/Autograd33FPCavity.png\" width=\"600\" alt=\"Rendering of the fabrication-ready chip with a zoom of the inverse designed reflector\">\n",
        "\n",
        "For more inverse design examples, please visit our [examples\n",
        "page](https://www.flexcompute.com/tidy3d/examples/). If you are new to\n",
        "the finite-difference time-domain (FDTD) method, we highly recommend\n",
        "going through our [FDTD101](https://www.flexcompute.com/fdtd101/)\n",
        "tutorials."
      ],
      "id": "6ff95f94-510f-4f77-a93e-598c26c301fa"
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {},
      "outputs": [],
      "source": [
        "import pickle\n",
        "\n",
        "import autograd.numpy as anp\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "from autograd.tracer import getval\n",
        "\n",
        "import tidy3d as td\n",
        "import tidy3d.web as web\n",
        "from tidy3d.plugins.autograd import adam, apply_updates, rescale, value_and_grad\n",
        "from tidy3d.plugins.autograd.invdes import (\n",
        "    make_conic_filter,\n",
        "    make_erosion_dilation_penalty,\n",
        "    symmetrize_mirror,\n",
        "    tanh_projection,\n",
        ")"
      ],
      "id": "aefeb000"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Inverse Design Setup <a name=\"1\"></a>\n",
        "\n",
        "We define the wavelength, the constant-index silicon and oxide used\n",
        "during optimization, and the design-region geometry. The design region\n",
        "is discretized into 20 nm pixels. The conic filter radius of 150 nm\n",
        "deliberately exceeds the 100 nm fabrication limit: the margin keeps the\n",
        "thresholded design legal."
      ],
      "id": "d6bb72bf-6a2e-4dd6-961e-7f097967233e"
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {},
      "outputs": [],
      "source": [
        "lda0 = 1.55                       # central wavelength (um)\n",
        "freq0 = td.C_0 / lda0\n",
        "fwidth = freq0 / 10\n",
        "eps_si = 3.48**2                  # constant-index materials for optimization\n",
        "eps_sio2 = 1.444**2\n",
        "\n",
        "wg_width = 0.50                   # SiEPIC-native single-mode strip width (um)\n",
        "t_slab = 0.22                     # silicon thickness (um)\n",
        "lx_des, ly_des = 3.0, 1.0         # design region (um)\n",
        "pix = 0.02                        # design pixel size (um)\n",
        "nx, ny = int(lx_des / pix), int(ly_des / pix)\n",
        "min_feature = 0.100               # required minimum feature size (um)\n",
        "filter_radius = 0.150             # conic filter radius (um), margin above min_feature\n",
        "etas = (0.45, 0.5, 0.55)          # dilated / nominal / eroded projections\n",
        "beta_final = 50.0\n",
        "num_steps = 35\n",
        "penalty_weight = 2.0\n",
        "\n",
        "Lx, Ly, Lz = 7.0, 3.4, 2.4        # simulation domain (um)\n",
        "x_src = -lx_des / 2 - 0.7\n",
        "x_refl = x_src - 0.5\n",
        "mode_plane = (0, 2.2, 1.6)\n",
        "mode_spec = td.ModeSpec(num_modes=1, target_neff=3.5)\n",
        "\n",
        "conic_filter = make_conic_filter(filter_radius, pix)\n",
        "penalty_fn = make_erosion_dilation_penalty(filter_radius, pix)"
      ],
      "id": "31c0485b"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "The reflector is a terminating mirror: the input waveguide extends\n",
        "through the boundary on one side and plain oxide lies beyond the design\n",
        "region on the other. We record the reflected fundamental mode with a\n",
        "[ModeMonitor](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.ModeMonitor.html)\n",
        "placed behind the\n",
        "[ModeSource](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.ModeSource.html),\n",
        "so it only sees the backward-propagating wave. A\n",
        "[MeshOverrideStructure](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.MeshOverrideStructure.html)\n",
        "locks the FDTD grid to the design pixels, and `symmetry=(0,-1,1)`\n",
        "exploits the two mirror symmetries of the TE mode for a fourfold cost\n",
        "reduction."
      ],
      "id": "d90ffc6b-62bf-4c1b-9018-dc57e91cf24b"
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {},
      "outputs": [],
      "source": [
        "def make_sim_base(msw: int = 18, override_dl=(pix, pix, pix), freqs=(freq0,), with_field: bool = False) -> td.Simulation:\n",
        "    \"\"\"Terminating-mirror FDTD without the design region.\"\"\"\n",
        "    wg_in = td.Structure(\n",
        "        geometry=td.Box.from_bounds(\n",
        "            rmin=(-1e3, -wg_width / 2, -t_slab / 2), rmax=(-lx_des / 2, wg_width / 2, t_slab / 2)\n",
        "        ),\n",
        "        medium=td.Medium(permittivity=eps_si),\n",
        "    )\n",
        "    mode_source = td.ModeSource(\n",
        "        source_time=td.GaussianPulse(freq0=freq0, fwidth=fwidth),\n",
        "        center=(x_src, 0, 0),\n",
        "        size=mode_plane,\n",
        "        mode_index=0,\n",
        "        mode_spec=mode_spec,\n",
        "        direction=\"+\",\n",
        "    )\n",
        "    monitors = [\n",
        "        td.ModeMonitor(center=(x_refl, 0, 0), size=mode_plane, freqs=list(freqs), mode_spec=mode_spec, name=\"refl\")\n",
        "    ]\n",
        "    if with_field:\n",
        "        monitors.append(\n",
        "            td.FieldMonitor(center=(0, 0, 0), size=(td.inf, td.inf, 0), freqs=[freq0], name=\"field\")\n",
        "        )\n",
        "    mesh = td.MeshOverrideStructure(\n",
        "        geometry=td.Box(center=(0, 0, 0), size=(lx_des, ly_des, t_slab)), dl=list(override_dl), enforce=True\n",
        "    )\n",
        "    return td.Simulation(\n",
        "        size=(Lx, Ly, Lz),\n",
        "        medium=td.Medium(permittivity=eps_sio2),\n",
        "        grid_spec=td.GridSpec.auto(min_steps_per_wvl=msw, wavelength=lda0, override_structures=[mesh]),\n",
        "        structures=[wg_in],\n",
        "        sources=[mode_source],\n",
        "        monitors=monitors,\n",
        "        run_time=1.5e-12,\n",
        "        boundary_spec=td.BoundarySpec.all_sides(boundary=td.PML()),\n",
        "        symmetry=(0, -1, 1),\n",
        "    )\n",
        "\n",
        "\n",
        "def make_design_structure(eps_2d) -> td.Structure:\n",
        "    \"\"\"Bind a (traced) permittivity array to the design-region box as a custom medium.\"\"\"\n",
        "    box = td.Box(center=(0, 0, 0), size=(lx_des, ly_des, t_slab))\n",
        "    return td.Structure.from_permittivity_array(geometry=box, eps_data=eps_2d.reshape((nx, ny, 1)))"
      ],
      "id": "0727c6f4"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Before submitting anything, we visualize the initial simulation with the\n",
        "uniform gray design to make sure all components are placed correctly."
      ],
      "id": "ad4e9833-9dad-4303-9a71-3e5089874465"
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {},
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
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OiSMzIESbfaFXr15xt5Ofnx9xBhMt2oiXlsdasmSJ3LZt27jbAyA/9dRTUdv7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\nCeZnfuZn/kExf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          }
        }
      ],
      "source": [
        "params0 = 0.5 * np.ones((nx, ny))\n",
        "d0 = tanh_projection(conic_filter(symmetrize_mirror(params0, axis=1)), 1.0, 0.5)\n",
        "sim_preview = make_sim_base().updated_copy(\n",
        "    structures=[*make_sim_base().structures, make_design_structure(rescale(d0, eps_sio2, eps_si))]\n",
        ")\n",
        "\n",
        "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 4), tight_layout=True)\n",
        "sim_preview.plot_eps(z=0, ax=ax1, freq=freq0)\n",
        "sim_preview.plot_eps(x=0, ax=ax2, freq=freq0)\n",
        "plt.show()"
      ],
      "id": "603c18b0"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "The objective is robust to fabrication bias and to the final\n",
        "thresholding step: each gradient evaluation simulates three versions of\n",
        "the design, projected at $\\eta = 0.45$, $0.5$, and $0.55$ (dilated,\n",
        "nominal, eroded), and averages their logarithmic reflection objectives.\n",
        "The average, rather than the worst case, keeps all three adjoint sources\n",
        "well conditioned. The erosion-dilation fabrication penalty is evaluated\n",
        "on the design padded with its physical surroundings (waveguide on the\n",
        "left, oxide on the right); without the padding the penalty cannot see\n",
        "sub-100 nm gaps pinched against the waveguide junction."
      ],
      "id": "5157a840-aebd-4b85-8869-d577c6df8630"
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {},
      "outputs": [],
      "source": [
        "aux = {}  # per-step diagnostics recorded by the objective\n",
        "\n",
        "\n",
        "def objective(params, beta: float, task_name: str) -> float:\n",
        "    \"\"\"Mean of log-reflection objectives over eroded/nominal/dilated projections, minus the fabrication penalty.\"\"\"\n",
        "    p = symmetrize_mirror(params, axis=1)\n",
        "    filtered = conic_filter(p)\n",
        "    sims, dens = {}, {}\n",
        "    for tag, eta in zip((\"dil\", \"nom\", \"ero\"), etas):\n",
        "        d = tanh_projection(filtered, beta, eta)\n",
        "        dens[tag] = d\n",
        "        base = make_sim_base()\n",
        "        sims[tag] = base.updated_copy(\n",
        "            structures=[*base.structures, make_design_structure(rescale(d, eps_sio2, eps_si))]\n",
        "        )\n",
        "    batch = web.run(sims, task_name=task_name, verbose=False)\n",
        "    Rs = [\n",
        "        anp.sum(anp.abs(batch[t][\"refl\"].amps.sel(direction=\"-\", f=freq0, mode_index=0).values) ** 2)\n",
        "        for t in (\"dil\", \"nom\", \"ero\")\n",
        "    ]\n",
        "    logs = [-anp.log10(1.0 - r + 1e-6) for r in Rs]\n",
        "    wg_col = (np.abs((np.arange(ny) - (ny - 1) / 2) * pix) <= wg_width / 2).astype(float)\n",
        "    padded = anp.concatenate([np.tile(wg_col, (8, 1)), dens[\"nom\"], np.zeros((8, ny))], axis=0)\n",
        "    pen = penalty_fn(padded)\n",
        "    weight = min(1.0, beta / 25.0) * penalty_weight\n",
        "    J = (logs[0] + logs[1] + logs[2]) / 3.0 - weight * pen\n",
        "    aux.update(R=float(getval(Rs[1])), R_worst=float(min(getval(r) for r in Rs)), pen=float(getval(pen)))\n",
        "    return J\n",
        "\n",
        "\n",
        "val_grad = value_and_grad(objective)"
      ],
      "id": "069e5bf8"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Now we run the optimization with the Adam optimizer and a linear $\\beta$\n",
        "ramp from 1 to 50, which gradually binarizes the design. The history is\n",
        "saved to disk after every iteration so a long run is never lost."
      ],
      "id": "386a37ad-8253-4e4b-8129-9f3e55b1fb6a"
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {},
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "step  1/35: J=-0.0432 R_nom=7.224% R_worst=4.974% pen=0.943\n",
            "step  2/35: J=+0.1725 R_nom=64.957% R_worst=36.793% pen=0.944\n",
            "step  3/35: J=+0.7432 R_nom=95.520% R_worst=78.261% pen=0.945\n",
            "step  4/35: J=+0.9899 R_nom=96.747% R_worst=94.849% pen=0.940\n",
            "step  5/35: J=+0.9679 R_nom=96.563% R_worst=96.033% pen=0.908\n",
            "step  6/35: J=+0.9516 R_nom=97.107% R_worst=96.662% pen=0.868\n",
            "step  7/35: J=+0.9415 R_nom=97.618% R_worst=96.473% pen=0.824\n",
            "step  8/35: J=+0.9641 R_nom=97.846% R_worst=97.201% pen=0.772\n",
            "step  9/35: J=+0.9654 R_nom=98.186% R_worst=97.481% pen=0.732\n",
            "step 10/35: J=+0.8974 R_nom=98.251% R_worst=97.627% pen=0.692\n",
            "step 11/35: J=+0.9393 R_nom=98.381% R_worst=97.923% pen=0.644\n",
            "step 12/35: J=+0.9283 R_nom=98.550% R_worst=97.875% pen=0.609\n",
            "step 13/35: J=+0.9045 R_nom=98.533% R_worst=98.034% pen=0.590\n",
            "step 14/35: J=+0.9398 R_nom=98.785% R_worst=98.435% pen=0.574\n",
            "step 15/35: J=+0.8925 R_nom=98.752% R_worst=98.292% pen=0.563\n",
            "step 16/35: J=+0.8716 R_nom=98.819% R_worst=98.372% pen=0.555\n",
            "step 17/35: J=+0.8456 R_nom=98.884% R_worst=98.437% pen=0.537\n",
            "step 18/35: J=+0.8600 R_nom=98.965% R_worst=98.566% pen=0.519\n",
            "step 19/35: J=+0.9052 R_nom=98.931% R_worst=98.647% pen=0.508\n",
            "step 20/35: J=+0.9544 R_nom=99.104% R_worst=98.561% pen=0.498\n",
            "step 21/35: J=+0.9854 R_nom=99.140% R_worst=98.630% pen=0.489\n",
            "step 22/35: J=+1.0054 R_nom=99.101% R_worst=98.832% pen=0.484\n",
            "step 23/35: J=+0.9949 R_nom=99.011% R_worst=98.805% pen=0.481\n",
            "step 24/35: J=+1.0595 R_nom=99.169% R_worst=98.902% pen=0.478\n",
            "step 25/35: J=+1.0470 R_nom=99.089% R_worst=98.888% pen=0.475\n",
            "step 26/35: J=+1.0572 R_nom=99.151% R_worst=98.825% pen=0.474\n",
            "step 27/35: J=+1.0694 R_nom=99.254% R_worst=98.890% pen=0.474\n",
            "step 28/35: J=+1.0763 R_nom=99.193% R_worst=98.896% pen=0.473\n",
            "step 29/35: J=+1.1068 R_nom=99.174% R_worst=99.022% pen=0.470\n",
            "step 30/35: J=+1.1033 R_nom=99.163% R_worst=99.049% pen=0.470\n",
            "step 31/35: J=+1.1164 R_nom=99.101% R_worst=99.026% pen=0.469\n",
            "step 32/35: J=+1.0979 R_nom=99.122% R_worst=99.037% pen=0.467\n",
            "step 33/35: J=+1.1095 R_nom=99.149% R_worst=98.986% pen=0.464\n",
            "step 34/35: J=+1.1142 R_nom=99.162% R_worst=98.982% pen=0.460\n",
            "step 35/35: J=+1.1239 R_nom=99.167% R_worst=98.995% pen=0.458"
          ]
        }
      ],
      "source": [
        "optimizer = adam(learning_rate=0.1)\n",
        "params = params0.copy()\n",
        "opt_state = optimizer.init(params)\n",
        "history = {\"J\": [], \"R\": [], \"R_worst\": [], \"pen\": [], \"params\": []}\n",
        "\n",
        "betas = np.linspace(1.0, beta_final, num_steps)\n",
        "for i in range(num_steps):\n",
        "    J, grad = val_grad(params, betas[i], task_name=f\"reflector_step_{i:03d}\")\n",
        "    updates, opt_state = optimizer.update(-grad, opt_state, params)  # -grad: maximize J\n",
        "    params[:] = apply_updates(params, updates)\n",
        "    np.clip(params, 0.0, 1.0, out=params)\n",
        "    history[\"J\"].append(float(J))\n",
        "    history[\"R\"].append(aux[\"R\"])\n",
        "    history[\"R_worst\"].append(aux[\"R_worst\"])\n",
        "    history[\"pen\"].append(aux[\"pen\"])\n",
        "    history[\"params\"].append(params.copy())\n",
        "    with open(\"reflector_history.pkl\", \"wb\") as f:\n",
        "        pickle.dump(history, f)\n",
        "    print(\n",
        "        f\"step {i+1:2d}/{num_steps}: J={J:+.4f} R_nom={100*aux['R']:.3f}% \"\n",
        "        f\"R_worst={100*aux['R_worst']:.3f}% pen={aux['pen']:.3f}\"\n",
        "    )"
      ],
      "id": "9e49e950"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "We plot the optimization history: the nominal and worst-case\n",
        "reflectivities converge together, which is exactly the robustness the\n",
        "three-projection objective buys."
      ],
      "id": "6ef49390-7125-49b8-881b-f10d51af052d"
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {},
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
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12PHZ1TEzfs7t3r9ecuMYGEPkyFbvllEVNkluul2vKOnK91o1lp6bJOU0PZYi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On+++9Xdna2Fi1apBUrVmjFihW2a5MmTTKsPeqoo7R8+XJFRER4Kz2g1ezHYaUyDgsA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\nYbQLDQ0N2m/XYSeVk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LXr6+vjaTdczuvvtuTZgwQVLu0py2ZniqiEjYgYV5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\n0ciRI5WdnS0PDw916dJF3t7e2rhxoxITEyVJTz75pN2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          }
        }
      ],
      "source": [
        "steps = np.arange(1, len(history[\"J\"]) + 1)\n",
        "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 4), tight_layout=True)\n",
        "ax1.plot(steps, history[\"J\"], \"-o\", ms=3)\n",
        "ax1.set_xlabel(\"Iteration\")\n",
        "ax1.set_ylabel(\"Objective\")\n",
        "ax1.grid()\n",
        "ax2.plot(steps, 100 * np.array(history[\"R\"]), \"-o\", ms=3, label=\"nominal\")\n",
        "ax2.plot(steps, 100 * np.array(history[\"R_worst\"]), \"-s\", ms=3, label=\"worst projection\")\n",
        "ax2.set_xlabel(\"Iteration\")\n",
        "ax2.set_ylabel(\"Reflectivity (%)\")\n",
        "ax2.legend()\n",
        "ax2.grid()\n",
        "plt.show()"
      ],
      "id": "2b035c84"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Binarization and Minimum Feature Size <a name=\"2\"></a>\n",
        "\n",
        "The optimized gray-scale density must become a fully binary\n",
        "silicon/oxide layout that respects the 100 nm minimum feature size\n",
        "everywhere, including against the waveguide junction. We implement the\n",
        "morphological checks with plain `numpy`: disk-based opening flags\n",
        "undersized silicon features, disk-based closing flags undersized oxide\n",
        "gaps, and a flood fill groups violations into clusters. Clusters smaller\n",
        "than 6 pixels are convex corner tips, which lithography rounds anyway;\n",
        "larger clusters are genuine violations that we repair directionally,\n",
        "filling undersized gaps and dilating thin walls."
      ],
      "id": "32e059a5-da17-4688-ac3d-b929af470902"
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {},
      "outputs": [],
      "source": [
        "pad = 8\n",
        "d100 = [(dx, dy) for dx in range(-3, 4) for dy in range(-3, 4) if dx**2 + dy**2 <= (min_feature / 2 / pix) ** 2 + 1e-9]\n",
        "d3x3 = [(dx, dy) for dx in range(-1, 2) for dy in range(-1, 2)]\n",
        "\n",
        "\n",
        "def shift(a, dx: int, dy: int):\n",
        "    \"\"\"Shift a 2D bool array, filling with False.\"\"\"\n",
        "    out = np.zeros_like(a)\n",
        "    out[max(dx, 0) : a.shape[0] + min(dx, 0), max(dy, 0) : a.shape[1] + min(dy, 0)] = a[\n",
        "        max(-dx, 0) : a.shape[0] + min(-dx, 0), max(-dy, 0) : a.shape[1] + min(-dy, 0)\n",
        "    ]\n",
        "    return out\n",
        "\n",
        "\n",
        "def dilate(a, offsets):\n",
        "    \"\"\"Morphological dilation by a structuring element given as offsets.\"\"\"\n",
        "    out = np.zeros_like(a)\n",
        "    for dx, dy in offsets:\n",
        "        out |= shift(a, dx, dy)\n",
        "    return out\n",
        "\n",
        "\n",
        "def erode(a, offsets):\n",
        "    \"\"\"Morphological erosion by a structuring element given as offsets.\"\"\"\n",
        "    out = np.ones_like(a)\n",
        "    for dx, dy in offsets:\n",
        "        out &= shift(a, dx, dy)\n",
        "    return out\n",
        "\n",
        "\n",
        "def label_components(a):\n",
        "    \"\"\"4-connected component labeling by flood fill.\"\"\"\n",
        "    lab = np.zeros(a.shape, int)\n",
        "    n = 0\n",
        "    for x0, y0 in zip(*np.where(a)):\n",
        "        if lab[x0, y0]:\n",
        "            continue\n",
        "        n += 1\n",
        "        stack = [(x0, y0)]\n",
        "        lab[x0, y0] = n\n",
        "        while stack:\n",
        "            x, y = stack.pop()\n",
        "            for dx, dy in ((1, 0), (-1, 0), (0, 1), (0, -1)):\n",
        "                u, v = x + dx, y + dy\n",
        "                if 0 <= u < a.shape[0] and 0 <= v < a.shape[1] and a[u, v] and not lab[u, v]:\n",
        "                    lab[u, v] = n\n",
        "                    stack.append((u, v))\n",
        "    return lab, n\n",
        "\n",
        "\n",
        "def embed(a):\n",
        "    \"\"\"Pad the design with its physical surroundings: waveguide on the left, oxide elsewhere.\"\"\"\n",
        "    wg_row = np.abs((np.arange(ny) - (ny - 1) / 2) * pix) <= wg_width / 2\n",
        "    big = np.zeros((a.shape[0] + 2 * pad, a.shape[1] + 2 * pad), bool)\n",
        "    big[pad:-pad, pad:-pad] = a\n",
        "    big[:pad, pad:-pad] = wg_row\n",
        "    return big\n",
        "\n",
        "\n",
        "def violation_clusters(a, min_px: int = 6):\n",
        "    \"\"\"Return sub-100 nm features as (kind, mask) pairs, ignoring small corner tips.\"\"\"\n",
        "    big = embed(a)\n",
        "    out = []\n",
        "    for kind, op in [(\"solid\", lambda x: dilate(erode(x, d100), d100)), (\"void\", lambda x: erode(dilate(x, d100), d100))]:\n",
        "        v = np.logical_xor(op(big), big)[pad:-pad, pad:-pad]\n",
        "        lab, n = label_components(v)\n",
        "        out += [(kind, lab == i) for i in range(1, n + 1) if (lab == i).sum() >= min_px]\n",
        "    return out\n",
        "\n",
        "\n",
        "def repair(b):\n",
        "    \"\"\"Repair violations directionally: fill undersized gaps, dilate thin silicon walls.\"\"\"\n",
        "    fixed = b.copy()\n",
        "    for _ in range(6):\n",
        "        clusters = violation_clusters(fixed)\n",
        "        if not clusters:\n",
        "            return fixed, True\n",
        "        lab_si, _ = label_components(fixed)\n",
        "        for kind, m in clusters:\n",
        "            if kind == \"void\":\n",
        "                fixed |= m\n",
        "            else:\n",
        "                wall_ids = np.unique(lab_si[m & fixed])\n",
        "                wall = np.isin(lab_si, wall_ids[wall_ids > 0])\n",
        "                for _ in range(2):\n",
        "                    wall = dilate(wall, d3x3)\n",
        "                fixed |= wall\n",
        "        fixed &= fixed[:, ::-1]\n",
        "    return fixed, not violation_clusters(fixed)"
      ],
      "id": "3afbdf9b"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "We threshold the best-performing snapshots at $\\eta = 0.5$ and keep the\n",
        "first one that is legal (or becomes legal after repair). The comparison\n",
        "below highlights the repair. In the top panel, red marks the features of\n",
        "the thresholded design that violate the 100 nm rule: two oxide slots\n",
        "pinched against the input waveguide corners (left edge) and a roughly 70\n",
        "nm thin silicon wall at the terminated end (right edge). In the bottom\n",
        "panel, orange marks the silicon the repair added: the slots are simply\n",
        "filled, while the thin wall triggers the dilate-the-whole-feature rule,\n",
        "so the entire trailing tooth is thickened by 40 nm and the orange traces\n",
        "its full outline."
      ],
      "id": "8949598f-08c0-49c1-bbb4-2396b8d35337"
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {},
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "snapshot 27: legal=True, repaired pixels=298"
          ]
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "image/png": 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          }
        }
      ],
      "source": [
        "design = None\n",
        "for step in sorted(range(len(history[\"params\"])), key=lambda i: -history[\"R\"][i])[:6]:\n",
        "    p = symmetrize_mirror(history[\"params\"][step], axis=1)\n",
        "    b = np.array(tanh_projection(conic_filter(p), beta_final, 0.5)) > 0.5\n",
        "    fixed, ok = repair(b)\n",
        "    print(f\"snapshot {step + 1}: legal={ok}, repaired pixels={np.logical_xor(fixed, b).sum()}\")\n",
        "    if ok:\n",
        "        design_raw, design = b, fixed\n",
        "        break\n",
        "\n",
        "violations = np.zeros_like(design_raw)\n",
        "for kind, m in violation_clusters(design_raw):\n",
        "    violations |= m\n",
        "modified = np.logical_xor(design_raw, design)\n",
        "\n",
        "\n",
        "def show_design(ax, base, highlight, color, title):\n",
        "    \"\"\"Render a binary design in grayscale with a highlighted pixel set in color.\"\"\"\n",
        "    rgb = np.ones((*base.shape, 3))\n",
        "    rgb[base] = [0.25, 0.25, 0.25]\n",
        "    rgb[highlight] = color\n",
        "    ax.imshow(np.transpose(rgb, (1, 0, 2)), origin=\"lower\",\n",
        "              extent=[-lx_des / 2, lx_des / 2, -ly_des / 2, ly_des / 2], interpolation=\"nearest\")\n",
        "    ax.set_xlabel(r\"x ($\\mu m$)\")\n",
        "    ax.set_ylabel(r\"y ($\\mu m$)\")\n",
        "    ax.set_title(title)\n",
        "\n",
        "\n",
        "from matplotlib.patches import Patch\n",
        "\n",
        "fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(9, 7), tight_layout=True)\n",
        "show_design(ax1, design_raw, violations, [0.85, 0.1, 0.1], \"Thresholded design, before repair\")\n",
        "ax1.legend(handles=[Patch(color=[0.25, 0.25, 0.25], label=\"silicon\"),\n",
        "                    Patch(color=[0.85, 0.1, 0.1], label=\"feature below 100 nm\")], loc=\"lower right\", fontsize=8)\n",
        "show_design(ax2, design, modified, [1.0, 0.6, 0.1], \"After repair\")\n",
        "ax2.legend(handles=[Patch(color=[0.25, 0.25, 0.25], label=\"silicon\"),\n",
        "                    Patch(color=[1.0, 0.6, 0.1], label=\"silicon added by repair\")], loc=\"lower right\", fontsize=8)\n",
        "plt.show()"
      ],
      "id": "36b6b4c6"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Verification and GDS Export <a name=\"3\"></a>\n",
        "\n",
        "We verify the final binary design with a broadband, high-accuracy\n",
        "simulation using a finer grid (`min_steps_per_wvl=30` and a 10 nm\n",
        "design-region mesh) and a\n",
        "[FieldMonitor](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.FieldMonitor.html)\n",
        "to inspect the mirror in action."
      ],
      "id": "eef8a2d2-af50-424d-89e5-b87862177e45"
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {},
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "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\">16:14:29 UTC </span>Created task <span style=\"color: #008000; text-decoration-color: #008000\">'reflector_verify'</span> with resource_id                   \n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'fdve-6b323c37-6e5f-4205-a987-5f6c26d5874f'</span> and task_type <span style=\"color: #008000; text-decoration-color: #008000\">'FDTD'</span>.  \n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "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-6b323c37-6e5f-4205-a987-5f6c26d5874f\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'https://tidy3d.simulation.cloud/workbench?taskId=fdve-6b323c37-6e5</span></a>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-6b323c37-6e5f-4205-a987-5f6c26d5874f\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">f-4205-a987-5f6c26d5874f'</span></a>.                                         \n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "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>Task folder: <a href=\"https://tidy3d.simulation.cloud/folders/9b36e144-ddb6-41f8-8dd8-30b62b26a870\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'default'</span></a>.                                            \n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<script type=\"application/vnd.jupyter.widget-view+json\">\n",
              "{\"model_id\":\"b2a8bce3df4744b29457d16ca2ab1ea9\",\"version_major\":2,\"version_minor\":0,\"quarto_mimetype\":\"application/vnd.jupyter.widget-view+json\"}\n",
              "</script>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "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>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "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\">16:14:31 UTC </span>Estimated FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.065</span>. This assumes the FDTD solver runs\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>for the full simulation time; if early shutoff is reached, the     \n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>billed cost can be lower. Use <span style=\"color: #008000; text-decoration-color: #008000\">'web.real_cost(task_id)'</span> to get the  \n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>billed FlexCredit cost after a simulation run.                     \n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "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\">16:14:33 UTC </span>status = queued                                                    \n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "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>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<script type=\"application/vnd.jupyter.widget-view+json\">\n",
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              "<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\">16:14:47 UTC </span>starting up solver                                                 \n",
              "</pre>"
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        {
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            "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",
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          "data": {
            "text/html": [
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              "{\"model_id\":\"08ce733b6d3048bcbda43fbf7affbd78\",\"version_major\":2,\"version_minor\":0,\"quarto_mimetype\":\"application/vnd.jupyter.widget-view+json\"}\n",
              "</script>"
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        {
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          "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\">16:15:00 UTC </span>early shutoff detected at <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">29</span>%, exiting.                            \n",
              "</pre>"
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        },
        {
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              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>"
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        {
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              "<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",
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              "{\"model_id\":\"9269aed1044b40a99cd8e5d037e10914\",\"version_major\":2,\"version_minor\":0,\"quarto_mimetype\":\"application/vnd.jupyter.widget-view+json\"}\n",
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        {
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              "<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\">16:15:04 UTC </span>status = success                                                   \n",
              "</pre>"
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        {
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          "data": {
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              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>"
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        {
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          "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\">16:15:06 UTC </span>View simulation result at                                          \n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-6b323c37-6e5f-4205-a987-5f6c26d5874f\" target=\"_blank\"><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">'https://tidy3d.simulation.cloud/workbench?taskId=fdve-6b323c37-6e5</span></a>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-6b323c37-6e5f-4205-a987-5f6c26d5874f\" target=\"_blank\"><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">f-4205-a987-5f6c26d5874f'</span></a><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">.</span>                                         \n",
              "</pre>"
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        },
        {
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          "metadata": {},
          "data": {
            "text/html": [
              "<script type=\"application/vnd.jupyter.widget-view+json\">\n",
              "{\"model_id\":\"9a611ac22a984636961e5819ded74b10\",\"version_major\":2,\"version_minor\":0,\"quarto_mimetype\":\"application/vnd.jupyter.widget-view+json\"}\n",
              "</script>"
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        {
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          "metadata": {},
          "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>"
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          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "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\">16:15:07 UTC </span>Loading results from data/reflector_verify.hdf5                    \n",
              "</pre>"
            ]
          }
        }
      ],
      "source": [
        "ldas = np.linspace(1.50, 1.60, 21)\n",
        "freqs = td.C_0 / ldas\n",
        "base = make_sim_base(msw=30, override_dl=(0.01, 0.01, 0.02), freqs=freqs, with_field=True)\n",
        "eps_final = eps_sio2 + (eps_si - eps_sio2) * design.astype(float)\n",
        "sim_verify = base.updated_copy(structures=[*base.structures, make_design_structure(eps_final)])\n",
        "\n",
        "sim_data = web.run(sim_verify, task_name=\"reflector_verify\", path=\"data/reflector_verify.hdf5\")"
      ],
      "id": "f0ebd693"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "The reflection spectrum stays high across the full band, and the field\n",
        "map shows the incident mode turned around within the first few teeth of\n",
        "the mirror."
      ],
      "id": "85dc26b6-dc4b-40dc-8418-92602716a1bb"
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {},
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
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3QMERERERERkdawFqyX64Pm+HjS4ShMJtKBxRJxLnr0Jp6ZOqnRbecsns8lj99MyHGJe4k66w8a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\nhwlaWSaXG2yd1pZG7S5bSNdMRESkNmtrtqHP1THbwtNPP80zzzzDW2+9x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N/P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\nrfmfLNSE7XctYlVA9zVixAh95zvf0Xe/+11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          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "R(1550 nm) = 98.64%"
          ]
        }
      ],
      "source": [
        "R = np.abs(sim_data[\"refl\"].amps.sel(direction=\"-\", mode_index=0).values) ** 2\n",
        "\n",
        "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(13, 4), tight_layout=True)\n",
        "ax1.plot(ldas, 10 * np.log10(R), \"-o\")\n",
        "ax1.set_xlabel(r\"Wavelength ($\\mu m$)\")\n",
        "ax1.set_ylabel(\"Reflection (dB)\")\n",
        "ax1.grid()\n",
        "sim_data.plot_field(field_monitor_name=\"field\", field_name=\"E\", val=\"abs^2\", f=freq0, ax=ax2)\n",
        "plt.show()\n",
        "\n",
        "i0 = np.argmin(np.abs(ldas - lda0))\n",
        "print(f\"R(1550 nm) = {100 * R[i0]:.2f}%\")"
      ],
      "id": "ffc12629"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Finally, we export the design to GDS directly on layer (1, 0), where the\n",
        "SiEPIC PDK expects the silicon geometry."
      ],
      "id": "6478a255-79cd-499b-a8c7-bde3f2205028"
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {},
      "outputs": [],
      "source": [
        "eps_final = eps_sio2 + (eps_si - eps_sio2) * design.astype(float)\n",
        "sim_export = make_sim_base().updated_copy(\n",
        "    structures=[*make_sim_base().structures, make_design_structure(eps_final)]\n",
        ")\n",
        "layer_map = {structure.medium: (1, 0) for structure in sim_export.structures}\n",
        "sim_export.to_gds_file(\n",
        "    fname=\"reflector.gds\", z=0, permittivity_threshold=(eps_sio2 + eps_si) / 2, frequency=freq0,\n",
        "    gds_layer_dtype_map=layer_map, gds_cell_name=\"REFLECTOR\",\n",
        ")"
      ],
      "id": "f59c5026"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Fabry-Perot Cavity Circuit on the SiEPIC PDK <a name=\"4\"></a>\n",
        "\n",
        "We now assemble the cavity circuit with\n",
        "[PhotonForge](https://docs.flexcompute.com/projects/photonforge/en/latest/index.html)\n",
        "using the SiEPIC OpenEBL technology. The reflector GDS becomes a\n",
        "component with one optical port and a Tidy3D S-matrix model. Two mirrors\n",
        "terminate a cavity waveguide that is side-coupled to a bus through an\n",
        "adiabatic S-bend coupler. The circuit is referenced at its two bus\n",
        "ports, so the simulation captures the pure device physics; the fiber\n",
        "interface is added later as layout only."
      ],
      "id": "3b0e0b80-8388-4157-8063-1e92b2a7994a"
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {},
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/plain": [
              "'Circuit'"
            ]
          }
        }
      ],
      "source": [
        "import photonforge as pf\n",
        "import siepic_forge as siepic\n",
        "\n",
        "pf.config.default_technology = siepic.ebeam()\n",
        "\n",
        "gap = 0.20              # coupling gap (um)\n",
        "coupling_length = 6.0\n",
        "arm_length = 15.0\n",
        "\n",
        "reflector = pf.find_top_level(*pf.load_layout(\"reflector.gds\").values())[0]\n",
        "reflector.name = \"REFLECTOR\"\n",
        "reflector.add_port(pf.Port((reflector.bounds()[0][0], 0.0), 0, \"TE_1550_500\"), port_name=\"P0\")\n",
        "reflector.add_model(pf.Tidy3DModel(symmetry=(0, -1, 0), verbose=False), \"Tidy3D\")\n",
        "\n",
        "coupler = pf.parametric.s_bend_straight_coupler(\n",
        "    port_spec=\"TE_1550_500\", coupling_distance=gap + wg_width, coupling_length=coupling_length,\n",
        "    s_bend_length=10.0, s_bend_offset=2.0, euler_fraction=0.5,\n",
        ")\n",
        "arm = pf.parametric.straight(port_spec=\"TE_1550_500\", length=arm_length)\n",
        "\n",
        "ports = dict(coupler.ports)\n",
        "cav_l, cav_r = sorted((n for n, p in ports.items() if abs(p.center[1]) < 0.2), key=lambda n: ports[n].center[0])\n",
        "bus_l, bus_r = sorted((n for n, p in ports.items() if abs(p.center[1]) >= 0.2), key=lambda n: ports[n].center[0])\n",
        "\n",
        "fp = pf.Component(\"FP_CAVITY\")\n",
        "coup = fp.add_reference(coupler)\n",
        "arm_l, arm_r = fp.add_reference(arm), fp.add_reference(arm)\n",
        "refl_l, refl_r = fp.add_reference(reflector), fp.add_reference(reflector)\n",
        "arm_l.connect(\"P1\", coup[cav_l])\n",
        "refl_l.connect(\"P0\", arm_l[\"P0\"])\n",
        "arm_r.connect(\"P0\", coup[cav_r])\n",
        "refl_r.connect(\"P0\", arm_r[\"P1\"])\n",
        "fp.add_port(coup[bus_l], port_name=\"P0\")\n",
        "fp.add_port(coup[bus_r], port_name=\"P1\")\n",
        "fp.add_model(pf.CircuitModel(verbose=False), \"Circuit\")"
      ],
      "id": "a8bb9814"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "The circuit S-matrix composes Tidy3D-computed S-matrices of the mirror\n",
        "and coupler with analytic waveguide models for the straight arms. On\n",
        "resonance, the side-coupled standing-wave cavity reflects the light back\n",
        "along the bus, so transmission dips and reflection peaks together."
      ],
      "id": "cadb9b94-9889-4157-bdc2-99e3f10ed22c"
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {},
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Starting…\n",
            "Uploading task 'Mode-StripTE1550nmw500nm…'\n",
            "Uploading task 'Mode-StripTE1550nmw500nm…'\n",
            "Uploading task 'Mode-StripTE1550nmw500nm…'\n",
            "Uploading task 'Mode-StripTE1550nmw500nm…'\n",
            "Uploading task 'P0@0…'\n",
            "Uploading task 'P1@0…'\n",
            "Uploading task 'P2@0…'\n",
            "Uploading task 'P3@0…'"
          ]
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "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\">16:15:11 UTC </span><span style=\"color: #800000; text-decoration-color: #800000\">WARNING: Structure at </span><span style=\"color: #008000; text-decoration-color: #008000\">'structures[13]'</span><span style=\"color: #800000; text-decoration-color: #800000\"> has bounds that extend      </span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">exactly to simulation edges. This can cause unexpected behavior. If</span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">intending to extend the structure to infinity along one dimension, </span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">use td.inf as a size variable instead to make this explicit.       </span>\n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "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><span style=\"color: #800000; text-decoration-color: #800000\">WARNING: Suppressed </span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span><span style=\"color: #800000; text-decoration-color: #800000\"> WARNING message.                             </span>\n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "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><span style=\"color: #800000; text-decoration-color: #800000\">WARNING: Structure at </span><span style=\"color: #008000; text-decoration-color: #008000\">'structures[13]'</span><span style=\"color: #800000; text-decoration-color: #800000\"> has bounds that extend      </span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">exactly to simulation edges. This can cause unexpected behavior. If</span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">intending to extend the structure to infinity along one dimension, </span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">use td.inf as a size variable instead to make this explicit.       </span>\n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "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><span style=\"color: #800000; text-decoration-color: #800000\">WARNING: Suppressed </span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span><span style=\"color: #800000; text-decoration-color: #800000\"> WARNING message.                             </span>\n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "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><span style=\"color: #800000; text-decoration-color: #800000\">WARNING: Structure at </span><span style=\"color: #008000; text-decoration-color: #008000\">'structures[13]'</span><span style=\"color: #800000; text-decoration-color: #800000\"> has bounds that extend      </span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">exactly to simulation edges. This can cause unexpected behavior. If</span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">intending to extend the structure to infinity along one dimension, </span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">use td.inf as a size variable instead to make this explicit.       </span>\n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "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><span style=\"color: #800000; text-decoration-color: #800000\">WARNING: Suppressed </span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span><span style=\"color: #800000; text-decoration-color: #800000\"> WARNING message.                             </span>\n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "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><span style=\"color: #800000; text-decoration-color: #800000\">WARNING: Structure at </span><span style=\"color: #008000; text-decoration-color: #008000\">'structures[13]'</span><span style=\"color: #800000; text-decoration-color: #800000\"> has bounds that extend      </span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">exactly to simulation edges. This can cause unexpected behavior. If</span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">intending to extend the structure to infinity along one dimension, </span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">use td.inf as a size variable instead to make this explicit.       </span>\n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "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><span style=\"color: #800000; text-decoration-color: #800000\">WARNING: Suppressed </span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span><span style=\"color: #800000; text-decoration-color: #800000\"> WARNING message.                             </span>\n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "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><span style=\"color: #800000; text-decoration-color: #800000\">WARNING: Structure at </span><span style=\"color: #008000; text-decoration-color: #008000\">'structures[13]'</span><span style=\"color: #800000; text-decoration-color: #800000\"> has bounds that extend      </span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">exactly to simulation edges. This can cause unexpected behavior. If</span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">intending to extend the structure to infinity along one dimension, </span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">use td.inf as a size variable instead to make this explicit.       </span>\n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "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><span style=\"color: #800000; text-decoration-color: #800000\">WARNING: Structure at </span><span style=\"color: #008000; text-decoration-color: #008000\">'structures[14]'</span><span style=\"color: #800000; text-decoration-color: #800000\"> has bounds that extend      </span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">exactly to simulation edges. This can cause unexpected behavior. If</span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">intending to extend the structure to infinity along one dimension, </span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">use td.inf as a size variable instead to make this explicit.       </span>\n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "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><span style=\"color: #800000; text-decoration-color: #800000\">WARNING: Structure at </span><span style=\"color: #008000; text-decoration-color: #008000\">'structures[13]'</span><span style=\"color: #800000; text-decoration-color: #800000\"> has bounds that extend      </span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">exactly to simulation edges. This can cause unexpected behavior. If</span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">intending to extend the structure to infinity along one dimension, </span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">use td.inf as a size variable instead to make this explicit.       </span>\n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "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><span style=\"color: #800000; text-decoration-color: #800000\">WARNING: Suppressed </span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span><span style=\"color: #800000; text-decoration-color: #800000\"> WARNING message.                             </span>\n",
              "</pre>"
            ]
          }
        }
      ],
      "source": [
        "wl_sweep = np.linspace(1.540, 1.560, 601)\n",
        "s = fp.s_matrix(td.C_0 / wl_sweep)\n",
        "wl = td.C_0 / np.array(s.frequencies) * 1e3\n",
        "order = np.argsort(wl)\n",
        "T = np.abs(np.array(s.elements[(\"P0@0\", \"P1@0\")]))[order] ** 2\n",
        "R_bus = np.abs(np.array(s.elements[(\"P0@0\", \"P0@0\")]))[order] ** 2\n",
        "wl = wl[order]\n",
        "\n",
        "dips = [i for i in range(1, len(T) - 1) if T[i] < T[i - 1] and T[i] < T[i + 1] and T[i] < 0.5 * np.median(T)]\n",
        "print(\"resonances (nm):\", np.round(wl[dips], 2))\n",
        "print(\"FSR (nm):\", np.round(np.diff(wl[dips]), 2))\n",
        "\n",
        "plt.figure(figsize=(9, 4.5))\n",
        "plt.plot(wl, 10 * np.log10(np.maximum(T, 1e-12)), label=\"$|S_{21}|^2$\")\n",
        "plt.plot(wl, 10 * np.log10(np.maximum(R_bus, 1e-12)), alpha=0.8, label=\"$|S_{11}|^2$\")\n",
        "plt.xlabel(\"Wavelength (nm)\")\n",
        "plt.ylabel(\"Power (dB)\")\n",
        "plt.legend()\n",
        "plt.grid()\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "id": "74907a37"
    }
  ],
  "nbformat": 4,
  "nbformat_minor": 5,
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    }
  }
}