{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Importing GDS files\n",
        "\n",
        "In Tidy3D, complex structures can be defined or imported from GDSII\n",
        "files using [Photonforge](https://www.flexcompute.com/photonforge/),\n",
        "Flexcompute’s photonic design automation tool. In this tutorial, we will\n",
        "illustrate how to use Photonforge to read a previously saved GDS file\n",
        "and import the structures into a simulation. For a tutorial on how to\n",
        "generate the GDS file used in this example, please refer to\n",
        "[this](https://www.flexcompute.com/tidy3d/examples/notebooks/GDSCreation/)\n",
        "notebook.\n",
        "\n",
        "<img src=\"img/splitter.png\" alt=\"Schematic of the directional coupler\" width=\"400\"/>\n",
        "\n",
        "Note that this tutorial requires Photonforge, so grab it with\n",
        "`pip install photonforge` before running the tutorial or uncomment the\n",
        "cell line below.\n",
        "\n",
        "We also provide a comprehensive list of other tutorials such as [how to\n",
        "define boundary\n",
        "conditions](https://www.flexcompute.com/tidy3d/examples/notebooks/BoundaryConditions/),\n",
        "[how to compute the S-matrix of a\n",
        "device](https://www.flexcompute.com/tidy3d/examples/notebooks/SMatrix/),\n",
        "[how to interact with tidy3d’s web\n",
        "API](https://www.flexcompute.com/tidy3d/examples/notebooks/WebAPI/), and\n",
        "[how to define self-intersecting\n",
        "polygons](https://www.flexcompute.com/tidy3d/examples/notebooks/SelfIntersectingPolyslab/).\n",
        "\n",
        "If you are new to the finite-difference time-domain (FDTD) method, we\n",
        "highly recommend going through our\n",
        "[FDTD101](https://www.flexcompute.com/fdtd101/) tutorials."
      ],
      "id": "033aecd2-706f-4a6f-854c-320dba97a1e3"
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {},
      "outputs": [],
      "source": [
        "# install photonforge\n",
        "# !pip install photonforge\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import photonforge as pf\n",
        "import tidy3d as td"
      ],
      "id": "46c561d8"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Loading a GDS file with Photonforge\n",
        "\n",
        "To load the geometry from a GDSII file, we use `pf.load_layout`, which\n",
        "reads the file and returns a dictionary of `Component` objects. We then\n",
        "use `pf.find_top_level` to automatically identify the top-level cell —\n",
        "the one that is not referenced as a sub-cell by any other cell in the\n",
        "file:"
      ],
      "id": "bcbb8950-502d-4716-9c48-ff89ec81b248"
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {},
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Top-level component: COUPLER\n",
            "Available cells: ['COUPLER', 'COUPLER_ARM']"
          ]
        }
      ],
      "source": [
        "gds_path = \"misc/coupler.gds\"\n",
        "\n",
        "# Load all components from the GDS file\n",
        "components = pf.load_layout(gds_path)\n",
        "\n",
        "# Identify the top-level component (not referenced by any other cell)\n",
        "top_level = pf.find_top_level(*components.values())[0]\n",
        "\n",
        "print(\"Top-level component:\", top_level.name)\n",
        "print(\"Available cells:\", list(components.keys()))"
      ],
      "id": "1e34f23a"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Inspecting Available Layers\n",
        "\n",
        "`get_structures()` extracts all 2D structures from the component\n",
        "(including flattened sub-cell references), grouped by\n",
        "`(layer, datatype)` pairs. This tells us which layers are present and\n",
        "how many structures each contains:"
      ],
      "id": "c721cfb9-d74a-4ba7-ac19-6e4d20e0b330"
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {},
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Layer (0, 0): 1 polygon(s)\n",
            "Layer (1, 0): 2 polygon(s)"
          ]
        }
      ],
      "source": [
        "structures_dict = top_level.get_structures()\n",
        "\n",
        "for (layer, dtype), polys in structures_dict.items():\n",
        "    print(f\"Layer ({layer}, {dtype}): {len(polys)} polygon(s)\")"
      ],
      "id": "3d959e16"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "We need to map each `(layer, datatype)` pair to a Tidy3D medium and\n",
        "vertical extent (`slab_bounds`). In this coupler layout, layer `(0, 0)`\n",
        "is the $\\text{SiO}_2$ substrate extending from z = -4 µm to z = 0, and\n",
        "layer `(1, 0)` contains the Si waveguide arms from z = 0 to z =\n",
        "`wg_height`.\n",
        "\n",
        "We can also define fabrication-related parameters. We consider the\n",
        "waveguide sidewalls to be slanted by `sidewall_angle` — positive values\n",
        "model the typical fabrication scenario where the base of the waveguide\n",
        "is wider than the top. A small `dilation` accounts for proximity\n",
        "effects."
      ],
      "id": "5810abfb-faed-4134-b708-2a70b140a248"
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Define waveguide height and fabrication parameters\n",
        "wg_height = 0.22\n",
        "dilation = 0.02\n",
        "sidewall_angle = np.deg2rad(10)\n",
        "\n",
        "# Define materials\n",
        "wg_n = 3.48  # Si refractive index\n",
        "sub_n = 1.45  # SiO2 refractive index\n",
        "medium_wg = td.Medium(permittivity=wg_n**2)\n",
        "medium_sub = td.Medium(permittivity=sub_n**2)\n",
        "\n",
        "# Map (layer, datatype) -> medium and extrusion parameters\n",
        "layer_map = {\n",
        "    (0, 0): dict(medium=medium_sub, slab_bounds=(-4.0, 0.0), sidewall_angle=0.0, dilation=0.0),\n",
        "    (1, 0): dict(\n",
        "        medium=medium_wg,\n",
        "        slab_bounds=(0.0, wg_height),\n",
        "        sidewall_angle=sidewall_angle,\n",
        "        dilation=dilation,\n",
        "    ),\n",
        "}"
      ],
      "id": "3ad48988"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Set up Geometries\n",
        "\n",
        "Each polygon from `get_structures()` exposes a `to_polygon().vertices`\n",
        "array that maps directly to `td.PolySlab`. We iterate over the layer\n",
        "map, skip any layers not in our map, and build a list of `td.Structure`\n",
        "objects. We also collect the arm geometries separately so we can compute\n",
        "the simulation bounding box from them later.\n",
        "\n",
        "A positive `sidewall_angle` on the waveguide arms models a tapered\n",
        "cross-section — the base (z = 0) is wider than the top (z = `wg_height`)\n",
        "by `dilation` on each side, which is typical of real fabrication."
      ],
      "id": "6e2f15f5-bfb6-44f9-8d8f-55abde2a749b"
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {},
      "outputs": [],
      "source": [
        "arm_geos = []\n",
        "structures = []\n",
        "\n",
        "for (layer, dtype), polys in structures_dict.items():\n",
        "    if (layer, dtype) not in layer_map:\n",
        "        continue\n",
        "    params = layer_map[(layer, dtype)]\n",
        "    for poly in polys:\n",
        "        geo = td.PolySlab(\n",
        "            vertices=poly.to_polygon().vertices,\n",
        "            axis=2,\n",
        "            slab_bounds=params[\"slab_bounds\"],\n",
        "            sidewall_angle=params[\"sidewall_angle\"],\n",
        "            dilation=params[\"dilation\"],\n",
        "        )\n",
        "        structures.append(td.Structure(geometry=geo, medium=params[\"medium\"]))\n",
        "        if (layer, dtype) == (1, 0):  # collect arm geometries for bounding box\n",
        "            arm_geos.append(geo)\n",
        "\n",
        "# Group arm geometries to compute simulation bounding box\n",
        "arms_geo = td.GeometryGroup(geometries=arm_geos)"
      ],
      "id": "c90f2253"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Let’s plot the base and the top of the coupler waveguide arms to make\n",
        "sure it looks ok. The base of the device should be larger than the top\n",
        "due to a positive `sidewall_angle`."
      ],
      "id": "9381a1c7-622b-41d2-b98d-40535247ff48"
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {},
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
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atEn33XefHn744ahAyjEwU2nj+8mSnpLA6mKFAyF5F1fI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mvIe8Atz54ClWUeOXhq3769BgwYoIEDB2rAgAFq1aqV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          }
        }
      ],
      "source": [
        "f, ax = plt.subplots(2, 1, figsize=(15, 6), tight_layout=True)\n",
        "arms_geo.plot(z=0.0, ax=ax[0])\n",
        "arms_geo.plot(z=wg_height, ax=ax[1])\n",
        "\n",
        "ax[0].set_ylim(-5, 5)\n",
        "_ = ax[1].set_ylim(-5, 5)"
      ],
      "id": "76f1bed6"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Set up Simulation\n",
        "\n",
        "Now let’s set up the rest of the Simulation."
      ],
      "id": "27b369a0-98fe-4265-82ef-f092348307e2"
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Spacing between waveguides and PML\n",
        "pml_spacing = 1.0\n",
        "\n",
        "# Simulation size\n",
        "sim_size = list(arms_geo.bounding_box.size)\n",
        "sim_size[0] -= 4 * pml_spacing\n",
        "sim_size[1] += 2 * pml_spacing\n",
        "sim_size[2] = wg_height + 2 * pml_spacing\n",
        "\n",
        "sim_center = list(arms_geo.bounding_box.center)\n",
        "sim_center[2] = 0\n",
        "\n",
        "# grid size in each direction\n",
        "dl = 0.020\n",
        "\n",
        "# ## Initialize and visualize simulation ###\n",
        "sim = td.Simulation(\n",
        "    size=sim_size,\n",
        "    center=sim_center,\n",
        "    grid_spec=td.GridSpec.uniform(dl=dl),\n",
        "    structures=structures,\n",
        "    run_time=2e-12,\n",
        "    boundary_spec=td.BoundarySpec.all_sides(boundary=td.PML()),\n",
        ")"
      ],
      "id": "5c0ec334"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Plot Simulation Geometry\n",
        "\n",
        "Let’s take a look at the simulation all together with the PolySlabs\n",
        "added. Here the angle of the sidewall deviating from the vertical\n",
        "direction is 30 degree."
      ],
      "id": "1b511fa2-490a-46e4-80af-b68eddf1c48c"
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {},
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "image/png": 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gKUmpqq//u/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          }
        }
      ],
      "source": [
        "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(17, 5))\n",
        "\n",
        "sim.plot(z=wg_height / 2, lw=1, edgecolor=\"k\", ax=ax1)\n",
        "sim.plot(x=0.1, lw=1, edgecolor=\"k\", ax=ax2)\n",
        "\n",
        "ax2.set_xlim([-3, 3])\n",
        "_ = ax2.set_ylim([-1, 1])"
      ],
      "id": "3f8fce17"
    }
  ],
  "nbformat": 4,
  "nbformat_minor": 5,
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    }
  }
}