{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ad2383e3",
   "metadata": {},
   "source": [
    "# Calibration-free 4×4 optical circuit switch: end-to-end design with Tidy3D and PhotonForge\n",
    "\n",
    "This notebook runs the whole design from the blog post *Designing a calibration-free optical circuit switch with\n",
    "Tidy3D and PhotonForge* and writes the final layout to GDS. The platform is 220 nm silicon in SiO2, TE polarization,\n",
    "1520–1580 nm. One section per design step:\n",
    "\n",
    "1. **Arm width** with the Tidy3D mode solver.\n",
    "2. **Components** with 3D FDTD and EME: S-bend, MMI, crossing and taper.\n",
    "3. **The 2×2 switch** in PhotonForge, with the heater from the Tidy3D heat solver.\n",
    "4. **No calibration**: Monte Carlo on the circuit.\n",
    "5. **The 4×4 fabric**: routing, netlist check, transmission and GDS.\n",
    "\n",
    "The FDTD, EME, heat and port-mode simulations run in the Tidy3D cloud, the FDTD ones in parallel; a fresh run takes\n",
    "about an hour. tidy3d keeps every result in its local cache, so a re-run loads them. Requires tidy3d 2.12 with\n",
    "tidy3d-extras (local mode solver) and photonforge 2.0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2fd9040d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-02T03:12:07.981983Z",
     "iopub.status.busy": "2026-11-02T03:12:07.981837Z",
     "iopub.status.idle": "2026-11-02T03:12:12.850302Z",
     "shell.execute_reply": "2026-11-02T03:12:12.849871Z"
    }
   },
   "outputs": [],
   "source": [
    "import time\n",
    "from pathlib import Path\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import photonforge as pf\n",
    "import tidy3d as td\n",
    "import tidy3d.web as web\n",
    "from matplotlib.patches import Polygon\n",
    "from scipy.integrate import cumulative_trapezoid\n",
    "from scipy.optimize import brentq\n",
    "\n",
    "td.config.logging.level = \"WARNING\"\n",
    "Path(\"data\").mkdir(exist_ok=True)\n",
    "plt.rcParams.update({\"figure.dpi\": 110, \"axes.spines.top\": False, \"axes.spines.right\": False})\n",
    "\n",
    "LDA0, T_SI = 1.55, 0.22                          # µm\n",
    "WL = np.linspace(1.52, 1.58, 31)                 # µm, the design band\n",
    "F0, FREQS, I0 = td.C_0 / LDA0, td.C_0 / WL, 15   # WL[I0] = 1.55 µm\n",
    "SI = td.material_library[\"cSi\"][\"Li1993_293K\"]\n",
    "SIO2 = td.material_library[\"SiO2\"][\"Palik_NoLoss\"]\n",
    "N_SI, N_SIO2 = (float(m.nk_model(F0)[0]) for m in (SI, SIO2))\n",
    "DN_DT_SI, DN_DT_SIO2 = 1.86e-4, 0.95e-5          # thermo-optic coefficients (1/K)\n",
    "db = lambda t: 10 * np.log10(np.abs(t) ** 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3044273a",
   "metadata": {},
   "source": [
    "**Technology.** A 220 nm Si core centred at z = 0 in SiO2, so TE runs can use a mirror plane at z = 0. `dilation`\n",
    "moves every Si edge outward; the sensitivity runs use it. Port names give the core width in nm."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3fe26a48",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-02T03:12:12.851688Z",
     "iopub.status.busy": "2026-11-02T03:12:12.851502Z",
     "iopub.status.idle": "2026-11-02T03:12:12.854874Z",
     "shell.execute_reply": "2026-11-02T03:12:12.854516Z"
    }
   },
   "outputs": [],
   "source": [
    "SI_LAYER, TIN_LAYER, METAL_LAYER = (1, 0), (10, 0), (11, 0)\n",
    "PORTS = {\"WG500\": (0.5, 2.5, 1), \"WG2000\": (2.0, 4.4, 3), \"WG2000n\": (2.0, 3.0, 3)}  # core and port widths (µm), TE modes\n",
    "\n",
    "\n",
    "@pf.parametric_technology\n",
    "def soi220(*, dilation: float = 0.0) -> pf.Technology:\n",
    "    layers = {\"Si\": pf.LayerSpec(SI_LAYER, \"Si core\", \"#d2132e18\", \"//\"),\n",
    "              \"TiN\": pf.LayerSpec(TIN_LAYER, \"TiN heater\", \"#e6a00060\", \"\\\\\"),\n",
    "              \"Metal\": pf.LayerSpec(METAL_LAYER, \"AlCu routing\", \"#ffd70060\", \"x\")}\n",
    "    extrusions = [pf.ExtrusionSpec(pf.MaskSpec(SI_LAYER, dilation=dilation), SI, (-T_SI / 2, T_SI / 2))]\n",
    "    ports = {k: pf.PortSpec(f\"{w} µm strip, {n} TE mode(s)\", pw, (-1.5, 1.5), num_modes=n, polarization=\"TE\",\n",
    "                            target_neff=N_SI, path_profiles=[(w, 0, SI_LAYER)], added_solver_modes=2)\n",
    "             for k, (w, pw, n) in PORTS.items()}\n",
    "    return pf.Technology(\"SOI 220 nm in SiO2\", \"1.0\", layers, extrusions, ports, SIO2)\n",
    "\n",
    "\n",
    "pf.config.default_technology = soi220()\n",
    "fdtd = lambda **kw: pf.Tidy3DModel(grid_spec=20, symmetry=(0, 0, 1), verbose=False, **kw)  # 20 steps per wavelength\n",
    "\n",
    "\n",
    "def show_layout(comp, ax):\n",
    "    \"\"\"Flattened layout: AlCu pads, Si core and TiN heaters.\"\"\"\n",
    "    for layer, color, alpha in ((METAL_LAYER, \"#BDBDBD\", 0.6), (SI_LAYER, \"#2F5D8C\", 1.0), (TIN_LAYER, \"#E69F00\", 0.8)):\n",
    "        for st in comp.get_structures(layer):\n",
    "            pols = st.to_polygon() if hasattr(st, \"to_polygon\") else st\n",
    "            for pol in pols if isinstance(pols, list) else [pols]:\n",
    "                ax.add_patch(Polygon(pol.vertices, fc=color, alpha=alpha, ec=\"none\"))\n",
    "    ax.autoscale_view()\n",
    "    ax.set(aspect=\"equal\", xlabel=\"x (µm)\", ylabel=\"y (µm)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c2621a8",
   "metadata": {},
   "source": [
    "## 1. Arm width: Tidy3D mode solver\n",
    "A phase error between the two arms of a Mach–Zehnder switch comes mostly from a width mismatch δw, through\n",
    "∂n_eff/∂w. That derivative falls steeply with width, and the local mode solver gives it for free."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "297002a1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-02T03:12:12.855729Z",
     "iopub.status.busy": "2026-11-02T03:12:12.855675Z",
     "iopub.status.idle": "2026-11-02T03:15:26.067947Z",
     "shell.execute_reply": "2026-11-02T03:15:26.067516Z"
    }
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 495x330 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "∂n_eff/∂w: 2.15e-03/nm at 0.45 µm, 2.57e-05/nm at 2 µm, 84× lower\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2 µm arm, TE0–TE2: n_eff [2.8203 2.7414 2.6051] | n_g [3.733 3.837 4.029] | dn/dT (1/K) [0.0001863 0.0001911 0.0002   ]\n"
     ]
    }
   ],
   "source": [
    "def modes(width, num_modes=1, wavelength=LDA0, core=SI, clad=SIO2):\n",
    "    \"\"\"n_eff of the TE modes of a Si strip in SiO2 (local mode solver, 10 nm grid).\"\"\"\n",
    "    sim = td.ModeSimulation(\n",
    "        size=(0, width + 5.0, 4.0), medium=clad, symmetry=(0, 0, 1),  # TE modes are even about z = 0\n",
    "        structures=[td.Structure(geometry=td.Box(size=(td.inf, width, T_SI)), medium=core)],\n",
    "        grid_spec=td.GridSpec(**{f\"grid_{a}\": td.UniformGrid(dl=0.01) for a in \"xyz\"}),\n",
    "        mode_spec=td.ModeSpec(num_modes=num_modes, target_neff=N_SI, precision=\"double\"), freqs=[td.C_0 / wavelength])\n",
    "    return sim.run_local().modes.n_eff.values.ravel()\n",
    "\n",
    "\n",
    "def guide(width, num_modes=1):\n",
    "    \"\"\"n_eff, group index and dn/dT of a strip's TE modes (λ0 ± 10 nm; core and cladding at ±10 K).\"\"\"\n",
    "    n = np.array([modes(width, num_modes, wavelength=lda) for lda in (LDA0 - 0.01, LDA0, LDA0 + 0.01)])\n",
    "    hot = lambda dT: dict(core=td.Medium(permittivity=(N_SI + DN_DT_SI * dT) ** 2),\n",
    "                          clad=td.Medium(permittivity=(N_SIO2 + DN_DT_SIO2 * dT) ** 2))\n",
    "    nt = np.array([modes(width, num_modes, **hot(dT)) for dT in (-10, 10)])\n",
    "    return n[1], n[1] - LDA0 * (n[2] - n[0]) / 0.02, (nt[1] - nt[0]) / 20\n",
    "\n",
    "\n",
    "widths = np.array([0.3, 0.35, 0.45, 0.6, 0.8, 1.0, 1.3, 1.6, 2.0, 2.5, 3.0])\n",
    "dndw = np.array([(modes(w + 0.005)[0] - modes(w - 0.005)[0]) / 10 for w in widths])  # per nm\n",
    "k045, k2 = int(np.flatnonzero(widths == 0.45)[0]), int(np.flatnonzero(widths == 2.0)[0])\n",
    "fig, ax = plt.subplots(figsize=(4.5, 3))\n",
    "ax.loglog(widths, dndw, \"o-\")\n",
    "ax.set(xlabel=\"Waveguide width (µm)\", ylabel=\"∂n$_{eff}$/∂w (nm$^{-1}$)\", title=\"TE0, 220 nm silicon strip\")\n",
    "plt.show()\n",
    "print(f\"∂n_eff/∂w: {dndw[k045]:.2e}/nm at 0.45 µm, {dndw[k2]:.2e}/nm at 2 µm, {dndw[k045] / dndw[k2]:.0f}× lower\")\n",
    "\n",
    "N_EFF, N_G, DN_DT = guide(2.0, 3)     # TE0–TE2 of the 2 µm arm: the phase-shifter model\n",
    "N_ROUTE, NG_ROUTE, _ = guide(0.5)     # 0.5 µm routing guide of the 4×4\n",
    "print(\"2 µm arm, TE0–TE2: n_eff\", N_EFF.round(4), \"| n_g\", N_G.round(3), \"| dn/dT (1/K)\", DN_DT.round(7))"
   ]
  },
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   "id": "671c16dd",
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    "The arms are 2 µm wide from MMI to MMI. A 2 µm guide also carries TE1 and TE2, so every component below is\n",
    "simulated with those modes on its 2 µm ports.\n",
    "\n",
    "## 2. Components: 3D FDTD and EME\n",
    "Four PhotonForge parametric components, each with a Tidy3D FDTD model:\n",
    "- **S-bend**, 2 µm wide, with curvature κ0·sin(2πs/L), zero at both ends and at the inflection. At a 5.5 µm rise\n",
    "  the TE1 it excites cancels at L = 34.5 µm.\n",
    "- **MMI**, 5.0 × 90.6 µm, with flush 2 µm access guides 3 µm apart.\n",
    "- **Crossing** (MMI style) and **taper** (2 → 0.5 µm over 10 µm) for the 4×4.\n",
    "\n",
    "The S-bend also runs with every edge moved by ±5 nm, for its phase sensitivity. All runs start now and continue in\n",
    "the cloud while the EME sweep below runs."
   ]
  },
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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\">22:15:26 EST </span><span style=\"color: #800000; text-decoration-color: #800000\">WARNING: Simulation final field decay value of </span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1.34e-05</span><span style=\"color: #800000; text-decoration-color: #800000\"> is greater </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">than the simulation shutoff threshold of </span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1e-05</span><span style=\"color: #800000; text-decoration-color: #800000\">. Consider running   </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">the simulation again with a larger </span><span style=\"color: #008000; text-decoration-color: #008000\">'run_time'</span><span style=\"color: #800000; text-decoration-color: #800000\"> duration for more    </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">accurate results.                                                  </span>\n",
       "</pre>\n"
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       "\u001b[2;36m22:15:26 EST\u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING: Simulation final field decay value of \u001b[0m\u001b[1;36m1.34e-05\u001b[0m\u001b[31m is greater \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31mthan the simulation shutoff threshold of \u001b[0m\u001b[1;36m1e-05\u001b[0m\u001b[31m. Consider running   \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31mthe simulation again with a larger \u001b[0m\u001b[32m'run_time'\u001b[0m\u001b[31m duration for more    \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31maccurate results.                                                  \u001b[0m\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\">22:15:27 EST </span><span style=\"color: #800000; text-decoration-color: #800000\">WARNING: Simulation final field decay value of </span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1.34e-05</span><span style=\"color: #800000; text-decoration-color: #800000\"> is greater </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">than the simulation shutoff threshold of </span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1e-05</span><span style=\"color: #800000; text-decoration-color: #800000\">. Consider running   </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">the simulation again with a larger </span><span style=\"color: #008000; text-decoration-color: #008000\">'run_time'</span><span style=\"color: #800000; text-decoration-color: #800000\"> duration for more    </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">accurate results.                                                  </span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m22:15:27 EST\u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING: Simulation final field decay value of \u001b[0m\u001b[1;36m1.34e-05\u001b[0m\u001b[31m is greater \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31mthan the simulation shutoff threshold of \u001b[0m\u001b[1;36m1e-05\u001b[0m\u001b[31m. Consider running   \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31mthe simulation again with a larger \u001b[0m\u001b[32m'run_time'\u001b[0m\u001b[31m duration for more    \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31maccurate results.                                                  \u001b[0m\n"
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   "source": [
    "FIELD = td.FieldMonitor(center=(0, 0, 0), size=(td.inf, td.inf, 0), freqs=[F0], fields=[\"Ex\", \"Ey\", \"Ez\"], name=\"field\")\n",
    "C4 = [(\"P1\", \"P2\", \"P3\", \"P0\"), (\"P2\", \"P3\", \"P0\", \"P1\"), (\"P3\", \"P0\", \"P1\", \"P2\")]  # the crossing's 90° rotations\n",
    "\n",
    "\n",
    "def ribbon(x, y, th, w):\n",
    "    \"\"\"Polygon of a path with heading th and width w.\"\"\"\n",
    "    nx, ny = -np.sin(th), np.cos(th)\n",
    "    return pf.Polygon(np.r_[np.c_[x + w / 2 * nx, y + w / 2 * ny], np.c_[x - w / 2 * nx, y - w / 2 * ny][::-1]])\n",
    "\n",
    "\n",
    "@pf.parametric_component\n",
    "def sbend(*, length=34.5, rise=5.5, technology=None):\n",
    "    \"\"\"2 µm S-bend with curvature κ0·sin(2πs/L); κ0 sets the rise.\"\"\"\n",
    "    s = np.linspace(0, length, 1201)\n",
    "    heading = lambda k0: k0 * length / (2 * np.pi) * (1 - np.cos(2 * np.pi * s / length))\n",
    "    k0 = brentq(lambda k0: np.trapezoid(np.sin(heading(k0)), s) - rise, 1e-6, np.pi ** 2 / (2 * length) * 0.999)\n",
    "    th = heading(k0)\n",
    "    x, y = cumulative_trapezoid(np.cos(th), s, initial=0), cumulative_trapezoid(np.sin(th), s, initial=0)\n",
    "    tech = technology or pf.config.default_technology\n",
    "    c = pf.Component(\"S-bend\", technology=tech)\n",
    "    c.add(\"Si\", ribbon(x, y, th, np.full_like(x, 2.0)))\n",
    "    c.add_port(pf.Port((0, 0), 0, tech.ports[\"WG2000\"]), port_name=\"P0\")\n",
    "    c.add_port(pf.Port((x[-1], y[-1]), 180, tech.ports[\"WG2000\"]), port_name=\"P1\")\n",
    "    c.add_model(fdtd(), \"Tidy3D\")\n",
    "    return c\n",
    "\n",
    "\n",
    "@pf.parametric_component\n",
    "def mmi(*, length=90.6, width=5.0, offset=1.5, port_length=1.5):\n",
    "    \"\"\"2×2 MMI with 2 µm access guides at ±offset: P0/P1 bottom/top left, P2/P3 bottom/top right.\"\"\"\n",
    "    tech = pf.config.default_technology\n",
    "    c = pf.Component(\"MMI\", technology=tech)\n",
    "    c.add(\"Si\", *pf.stencil.mmi(length, width, 2, port_length, 2.0, port_separation=2 * offset))\n",
    "    (x0, _), (x1, _) = c.bounds()\n",
    "    for name, x, y, a in ((\"P0\", x0, -offset, 0), (\"P1\", x0, offset, 0), (\"P2\", x1, -offset, 180), (\"P3\", x1, offset, 180)):\n",
    "        c.add_port(pf.Port((x, y), a, tech.ports[\"WG2000n\"]), port_name=name)\n",
    "    c.add_model(fdtd(bounds=((None, -4.5, None), (None, 4.5, None)), monitors=[FIELD]), \"Tidy3D\")\n",
    "    return c\n",
    "\n",
    "\n",
    "bend, core = sbend(), mmi()\n",
    "cross = pf.parametric.crossing(port_spec=\"WG500\", arm_length=4.8, added_width=\"1.05*min(1, u/0.35)\",\n",
    "                               model=fdtd(port_symmetries=C4, monitors=[FIELD]))\n",
    "taper = pf.parametric.transition(port_spec1=\"WG2000\", port_spec2=\"WG500\", length=10.0, profile=\"u^2\", model=fdtd())\n",
    "INPUTS = {\"bend\": [\"P0@0\", \"P0@1\", \"P0@2\"], \"mmi\": [f\"P{p}@{m}\" for p in (0, 1) for m in range(3)], \"cross\": [\"P0@0\"],\n",
    "          \"taper\": [\"P0@0\", \"P0@1\", \"P0@2\", \"P1@0\"], \"bend+5\": [\"P0@0\"], \"bend-5\": [\"P0@0\"]}\n",
    "jobs = {\"bend\": bend, \"mmi\": core, \"cross\": cross, \"taper\": taper,\n",
    "        **{f\"bend{d:+d}\": sbend(technology=soi220(dilation=d * 1e-3)) for d in (5, -5)}}\n",
    "runs = {k: c.active_model.start(c, FREQS, inputs=INPUTS[k], verbose=False) for k, c in jobs.items()}"
   ]
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    "### MMI length: EME sweep\n",
    "A y-symmetric MMI splits into an even and an odd problem. With a_e and a_o their TE0 transmissions, an MZI built\n",
    "from two such MMIs leaks |a_e² + a_o²|² into its bar port at zero power and sends |a_e² − a_o²|² to the cross port.\n",
    "Any mismatch between the two images, in focus or in phase, costs extinction ratio (ER). EME solves each parity once\n",
    "and sweeps the length almost for free. A 3D FDTD check of three lengths around the EME optimum (not repeated here)\n",
    "then set the final length, 90.6 µm."
   ]
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",
      "text/plain": [
       "<Figure size 880x308 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EME optimum: 90.8 µm, worst-case ER 38.6 dB\n"
     ]
    }
   ],
   "source": [
    "def mmi_eme(parity, W=5.0, L=91.0, offset=1.5, lengths=np.arange(88.0, 94.01, 0.2)):\n",
    "    \"\"\"EME of the MMI for one y-parity (+1: even TE0 supermode), sweeping the body length.\"\"\"\n",
    "    box = lambda x0, x1, y, w: td.Structure(geometry=td.Box.from_bounds((x0, y - w / 2, -T_SI / 2), (x1, y + w / 2, T_SI / 2)), medium=SI)\n",
    "    structures = [box(0, L, 0, W)] + [box(-3, L + 3, y, 2.0) for y in (-offset, offset)]\n",
    "    io, body = td.EMEModeSpec(num_modes=6), td.EMEModeSpec(num_modes=40)\n",
    "    return td.EMESimulation(\n",
    "        center=(L / 2, 0, 0), size=(L + 4, W + 4, 3.0), medium=SIO2, structures=structures, axis=0,\n",
    "        freqs=list(td.C_0 / np.linspace(1.52, 1.58, 7)), symmetry=(0, -parity, 1),\n",
    "        eme_grid_spec=td.EMEExplicitGrid(boundaries=[0.0, L], mode_specs=[io, body, io]),\n",
    "        sweep_spec=td.EMELengthSweep(scale_factors=np.c_[np.ones_like(lengths), lengths / L, np.ones_like(lengths)]),\n",
    "        grid_spec=td.GridSpec.auto(wavelength=LDA0, min_steps_per_wvl=25))\n",
    "\n",
    "\n",
    "lengths = np.arange(88.0, 94.01, 0.2)\n",
    "eme = web.Batch(simulations={f\"mmi_eme_{p:+d}\": mmi_eme(p) for p in (1, -1)}, verbose=False).run(path_dir=\"data\")\n",
    "ae, ao = (eme[f\"mmi_eme_{p:+d}\"].smatrix.S21.isel(mode_index_out=0, mode_index_in=0).values for p in (1, -1))\n",
    "er_eme = (10 * np.log10(abs(ae ** 2 - ao ** 2) ** 2 / abs(ae ** 2 + ao ** 2) ** 2)).min(0)  # worst over the band\n",
    "el_eme = (-10 * np.log10((abs(ae) ** 2 + abs(ao) ** 2) / 2)).max(0)\n",
    "fig, ax = plt.subplots(1, 2, figsize=(8, 2.8))\n",
    "ax[0].plot(lengths, er_eme)\n",
    "ax[1].plot(lengths, el_eme)\n",
    "ax[0].set(xlabel=\"MMI length (µm)\", ylabel=\"Worst-case zero-power ER (dB)\")\n",
    "ax[1].set(xlabel=\"MMI length (µm)\", ylabel=\"Worst-case excess loss (dB)\")\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "print(f\"EME optimum: {lengths[er_eme.argmax()]:.1f} µm, worst-case ER {er_eme.max():.1f} dB\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d79d046e",
   "metadata": {},
   "source": [
    "### FDTD results\n",
    "Each FDTD S-matrix becomes the component's circuit model. Inputs that were not simulated are filled by reciprocity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "62618102",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-02T03:15:29.206507Z",
     "iopub.status.busy": "2026-11-02T03:15:29.206422Z",
     "iopub.status.idle": "2026-11-02T03:15:34.059159Z",
     "shell.execute_reply": "2026-11-02T03:15:34.058711Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "S-bend: TE0 loss 0.0015 dB at 1550 nm, 0.0079 dB worst; TE1 ≤ -26.6 dB, TE2 ≤ -53.3 dB\n",
      "MMI: excess loss 0.042 dB at 1550 nm, 0.193 dB worst; split 0.498 / 0.492, 89.9° apart\n",
      "Crossing: loss 0.043 dB at 1550 nm, 0.056 dB worst; crosstalk ≤ -43.5 dB\n",
      "Taper: TE0 loss 0.0136 dB at 1550 nm, 0.0136 dB worst\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 990x253 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def wait(runs):\n",
    "    \"\"\"Block until every FDTD run has finished; return the S-matrices.\"\"\"\n",
    "    while any(r.status[\"message\"] not in (\"success\", \"error\") for r in runs.values()):\n",
    "        time.sleep(30)\n",
    "    failed = {k: r.status for k, r in runs.items() if r.status[\"message\"] != \"success\"}\n",
    "    assert not failed, failed\n",
    "    return {k: r.s_matrix for k, r in runs.items()}\n",
    "\n",
    "\n",
    "def data_model(comp, s, inputs=None):\n",
    "    \"\"\"Make an FDTD S-matrix the component's model, keeping the modes each port defines. Given the simulated `inputs`,\n",
    "    every reciprocal pair is averaged and the other inputs are filled by reciprocity. Without them the S-matrix is used\n",
    "    as is: a port-symmetry fill (the crossing) can flip the sign of S_ba, and averaging would cancel its crosstalk.\"\"\"\n",
    "    n = {p: port.spec.num_modes for p, port in comp.ports.items()}\n",
    "    ok = lambda q: int(q.split(\"@\")[1]) < n[q.split(\"@\")[0]]\n",
    "    el = {}\n",
    "    for (a, b), v in s.elements.items():\n",
    "        if not (ok(a) and ok(b)):\n",
    "            continue\n",
    "        if inputs is None:\n",
    "            el[(a, b)] = v\n",
    "        elif a in inputs:\n",
    "            el[(a, b)] = el[(b, a)] = (v + (s.elements[(b, a)] if b in inputs else v)) / 2\n",
    "    comp.add_model(pf.DataModel(pf.SMatrix(s.frequencies, el)), \"Data\")\n",
    "\n",
    "\n",
    "S = wait(runs)\n",
    "for k, c in ((\"bend\", bend), (\"mmi\", core), (\"cross\", cross), (\"taper\", taper)):\n",
    "    data_model(c, S[k], None if k == \"cross\" else INPUTS[k])  # the crossing's 4-fold symmetry fills its other inputs\n",
    "\n",
    "e = S[\"bend\"].elements\n",
    "loss = -db(e[(\"P0@0\", \"P1@0\")])\n",
    "print(f\"S-bend: TE0 loss {loss[I0]:.4f} dB at 1550 nm, {loss.max():.4f} dB worst; \"\n",
    "      f\"TE1 ≤ {db(e[('P0@0', 'P1@1')]).max():.1f} dB, TE2 ≤ {db(e[('P0@0', 'P1@2')]).max():.1f} dB\")\n",
    "e = S[\"mmi\"].elements\n",
    "b, c = e[(\"P1@0\", \"P3@0\")], e[(\"P1@0\", \"P2@0\")]\n",
    "loss = -10 * np.log10(abs(b) ** 2 + abs(c) ** 2)\n",
    "print(f\"MMI: excess loss {loss[I0]:.3f} dB at 1550 nm, {loss.max():.3f} dB worst; split {abs(b[I0]) ** 2:.3f} / \"\n",
    "      f\"{abs(c[I0]) ** 2:.3f}, {abs(np.angle(c[I0] / b[I0], deg=True)):.1f}° apart\")\n",
    "e = S[\"cross\"].elements\n",
    "loss = -db(e[(\"P0@0\", \"P2@0\")])\n",
    "print(f\"Crossing: loss {loss[I0]:.3f} dB at 1550 nm, {loss.max():.3f} dB worst; crosstalk ≤ \"\n",
    "      f\"{max(db(e[('P0@0', 'P1@0')]).max(), db(e[('P0@0', 'P3@0')]).max()):.1f} dB\")\n",
    "loss = -db(S[\"taper\"].elements[(\"P0@0\", \"P1@0\")])\n",
    "print(f\"Taper: TE0 loss {loss[I0]:.4f} dB at 1550 nm, {loss.max():.4f} dB worst\")\n",
    "\n",
    "fd = runs[\"mmi\"].runners[\"P1@0\"].data[\"field\"]\n",
    "E = np.sqrt(sum(abs(fd.field_components[q].isel(f=0).squeeze()) ** 2 for q in (\"Ex\", \"Ey\", \"Ez\")))\n",
    "fig, ax = plt.subplots(figsize=(9, 2.3))\n",
    "ax.pcolormesh(E.x, E.y, (E / E.max()).T, cmap=\"magma\", shading=\"auto\", rasterized=True)\n",
    "ax.set(xlim=(-46.8, 46.8), ylim=(-3.5, 3.5), aspect=3, xlabel=\"x (µm)\", ylabel=\"y (µm)\",\n",
    "       title=\"MMI, TE0 into the top input: |E| at 1550 nm (y stretched 3×)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7f23a45",
   "metadata": {},
   "source": [
    "## 3. The 2×2 switch in PhotonForge\n",
    "The splitter is the MMI with an S-bend on each port, which puts the arms 14 µm apart. Each arm is a 2 µm phase\n",
    "shifter under a TiN heater, with an analytic TE0–TE2 model from section 1. PhotonForge's CircuitModel cascades\n",
    "everything with three modes on every 2 µm port."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "471a968b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-02T03:15:34.060381Z",
     "iopub.status.busy": "2026-11-02T03:15:34.060311Z",
     "iopub.status.idle": "2026-11-02T03:15:34.067151Z",
     "shell.execute_reply": "2026-11-02T03:15:34.066882Z"
    }
   },
   "outputs": [],
   "source": [
    "T0 = 293.0  # K\n",
    "\n",
    "\n",
    "def splitter(core, bend):\n",
    "    \"\"\"MMI with a mirrored S-bend on every port; port order as in the MMI.\"\"\"\n",
    "    c = pf.Component(\"Splitter\")\n",
    "    m = c.add_reference(core)\n",
    "    for port, mirror in ((\"P0\", False), (\"P1\", True), (\"P2\", True), (\"P3\", False)):\n",
    "        r = c.add_reference(pf.Reference(bend, x_reflection=mirror))\n",
    "        r.connect(\"P0\", m[port])\n",
    "        c.add_port(r[\"P1\"], port_name=port)\n",
    "    c.add_model(pf.CircuitModel(), \"Circuit\")\n",
    "    return c\n",
    "\n",
    "\n",
    "@pf.parametric_component\n",
    "def phase_shifter(*, length=31.75, temperature=T0, dn=0.0):\n",
    "    \"\"\"2 µm arm under a 3 µm TiN heater: heating through `temperature`, an arm phase error through `dn`.\"\"\"\n",
    "    tech = pf.config.default_technology\n",
    "    c = pf.Component(\"Phase shifter\", technology=tech)\n",
    "    c.add(\"Si\", pf.Rectangle(corner1=(0, -1.0), corner2=(length, 1.0)))\n",
    "    c.add(\"TiN\", pf.Rectangle(corner1=(0, -1.5), corner2=(length, 1.5)))\n",
    "    c.add_port(pf.Port((0, 0), 0, tech.ports[\"WG2000\"]), port_name=\"P0\")\n",
    "    c.add_port(pf.Port((length, 0), 180, tech.ports[\"WG2000\"]), port_name=\"P1\")\n",
    "    c.add_model(pf.AnalyticWaveguideModel(n_eff=[float(n + dn) for n in N_EFF], n_group=[float(n) for n in N_G],\n",
    "                                          dn_dT=[float(v) for v in DN_DT], length=length, temperature=temperature,\n",
    "                                          reference_frequency=F0), \"Analytic\")\n",
    "    return c\n",
    "\n",
    "\n",
    "def mzs(split, top, bottom, pads=True, name=\"2x2 switch\"):\n",
    "    \"\"\"Splitter, two phase shifters, splitter. Ports I1/I2 (top/bottom left) and O1/O2 (right): with equal arms the\n",
    "    light crosses, I1 → O2. pads: TiN leads from each heater's far edge (a full L) to two 40 µm AlCu pads.\"\"\"\n",
    "    c = pf.Component(name)\n",
    "    a = c.add_reference(split)\n",
    "    arms = [c.add_reference(top), c.add_reference(bottom)]\n",
    "    arms[0].connect(\"P0\", a[\"P3\"])\n",
    "    arms[1].connect(\"P0\", a[\"P2\"])\n",
    "    b = c.add_reference(split)\n",
    "    b.connect(\"P1\", arms[0][\"P1\"])\n",
    "    for name, ref, port in ((\"I1\", a, \"P1\"), (\"I2\", a, \"P0\"), (\"O1\", b, \"P3\"), (\"O2\", b, \"P2\")):\n",
    "        c.add_port(ref[port], port_name=name)\n",
    "    for arm, s in zip(arms, (1, -1)) if pads else ():\n",
    "        (x0, y), (x1, _) = arm[\"P0\"].center, arm[\"P1\"].center\n",
    "        for xe, xp in ((x0, x0 - 22.0), (x1, x1 + 22.0)):\n",
    "            c.add(\"TiN\", pf.Path((xe, y - 1.5 * s), width=4.0).segment((xe, y + 10 * s)).segment((xp, y + 10 * s)).segment((xp, y + 35 * s)))\n",
    "            c.add(\"Metal\", pf.Rectangle(center=(xp, y + 50 * s), size=(40.0, 40.0)))\n",
    "    c.add_model(pf.CircuitModel(), \"Circuit\")\n",
    "    return c\n",
    "\n",
    "\n",
    "split = splitter(core, bend)\n",
    "transmission = lambda c, freqs=FREQS: c.s_matrix(freqs, show_progress=False).elements"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9d78513",
   "metadata": {},
   "source": [
    "**Phase-shifter length.** TE1 and TE2 from the MMI reach the second MMI with a phase that depends on the straight\n",
    "length between the S-bends, so the worst-case loss over the band swings with the phase-shifter length."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f06f5441",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-02T03:15:34.068112Z",
     "iopub.status.busy": "2026-11-02T03:15:34.068047Z",
     "iopub.status.idle": "2026-11-02T03:15:49.792700Z",
     "shell.execute_reply": "2026-11-02T03:15:49.792316Z"
    },
    "lines_to_next_cell": 1
   },
   "outputs": [
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       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>at                                                                 \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=mo-f2a4e682-ea98-4549-9b86-2693d9636cc9\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'https://tidy3d.simulation.cloud/workbench?taskId=mo-f2a4e682-ea98-</span></a>\n",
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       "\u001b[2;36m22:15:34 EST\u001b[0m\u001b[2;36m \u001b[0mLoading simulation from local cache. View cached task using web UI \n",
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       "\u001b[2;36m             \u001b[0m\u001b]8;id=2384511;https://tidy3d.simulation.cloud/workbench?taskId=mo-f2a4e682-ea98-4549-9b86-2693d9636cc9\u001b\\\u001b[32m4549-9b86-2693d9636cc9'\u001b[0m\u001b]8;;\u001b\\.                                           \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\">             </span>Loading simulation from local cache. View cached task using web UI \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>at                                                                 \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=mo-95bf395b-7208-4392-b941-9b3af5b78475\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'https://tidy3d.simulation.cloud/workbench?taskId=mo-95bf395b-7208-</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=mo-95bf395b-7208-4392-b941-9b3af5b78475\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">4392-b941-9b3af5b78475'</span></a>.                                           \n",
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       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mLoading simulation from local cache. View cached task using web UI \n",
       "\u001b[2;36m             \u001b[0mat                                                                 \n",
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",
      "text/plain": [
       "<Figure size 495x330 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "phase-shifter length: 31.75 µm (0.26 dB worst-case loss; up to 0.50 dB in the scan)\n"
     ]
    }
   ],
   "source": [
    "Ls = np.arange(26.0, 38.01, 0.25)\n",
    "worst = [(-db(transmission(mzs(split, phase_shifter(length=L), phase_shifter(length=L), pads=False))[(\"I1@0\", \"O2@0\")])).max()\n",
    "         for L in Ls]\n",
    "L_PS = float(Ls[int(np.argmin(worst))])\n",
    "fig, ax = plt.subplots(figsize=(4.5, 3))\n",
    "ax.plot(Ls, worst)\n",
    "ax.axvline(L_PS, color=\"0.5\", ls=\":\")\n",
    "ax.set(xlabel=\"Phase-shifter length (µm)\", ylabel=\"Worst-case Off-state loss (dB)\")\n",
    "plt.show()\n",
    "print(f\"phase-shifter length: {L_PS} µm ({min(worst):.2f} dB worst-case loss; up to {max(worst):.2f} dB in the scan)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b836729e",
   "metadata": {},
   "source": [
    "**Heater: Tidy3D heat solver.** The arm phase depends only on the temperature integrated along the arm, which a 2D\n",
    "cross-section gives exactly for a straight arm. The 3 µm × 120 nm TiN heater sits 1.5 µm above the core."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "95bb6cce",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-02T03:15:49.794019Z",
     "iopub.status.busy": "2026-11-02T03:15:49.793924Z",
     "iopub.status.idle": "2026-11-02T03:15:53.591999Z",
     "shell.execute_reply": "2026-11-02T03:15:53.591654Z"
    }
   },
   "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\">22:15:49 EST </span><span style=\"color: #800000; text-decoration-color: #800000\">WARNING: Structure at </span><span style=\"color: #008000; text-decoration-color: #008000\">'structures[0]'</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>\n"
      ],
      "text/plain": [
       "\u001b[2;36m22:15:49 EST\u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING: Structure at \u001b[0m\u001b[32m'structures\u001b[0m\u001b[32m[\u001b[0m\u001b[32m0\u001b[0m\u001b[32m]\u001b[0m\u001b[32m'\u001b[0m\u001b[31m has bounds that extend       \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31mexactly to simulation edges. This can cause unexpected behavior. If\u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31mintending to extend the structure to infinity along one dimension, \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31muse td.inf as a size variable instead to make this explicit.       \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><span style=\"color: #800000; text-decoration-color: #800000\">WARNING: Structure at </span><span style=\"color: #008000; text-decoration-color: #008000\">'structures[0]'</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>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING: Structure at \u001b[0m\u001b[32m'structures\u001b[0m\u001b[32m[\u001b[0m\u001b[32m0\u001b[0m\u001b[32m]\u001b[0m\u001b[32m'\u001b[0m\u001b[31m has bounds that extend       \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31mexactly to simulation edges. This can cause unexpected behavior. If\u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31mintending to extend the structure to infinity along one dimension, \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31muse td.inf as a size variable instead to make this explicit.       \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><span style=\"color: #800000; text-decoration-color: #800000\">WARNING: Structure at </span><span style=\"color: #008000; text-decoration-color: #008000\">'structures[0]'</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>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING: Structure at \u001b[0m\u001b[32m'structures\u001b[0m\u001b[32m[\u001b[0m\u001b[32m0\u001b[0m\u001b[32m]\u001b[0m\u001b[32m'\u001b[0m\u001b[31m has bounds that extend       \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31mexactly to simulation edges. This can cause unexpected behavior. If\u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31mintending to extend the structure to infinity along one dimension, \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31muse td.inf as a size variable instead to make this explicit.       \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><span style=\"color: #800000; text-decoration-color: #800000\">WARNING: Structure at </span><span style=\"color: #008000; text-decoration-color: #008000\">'structures[0]'</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>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING: Structure at \u001b[0m\u001b[32m'structures\u001b[0m\u001b[32m[\u001b[0m\u001b[32m0\u001b[0m\u001b[32m]\u001b[0m\u001b[32m'\u001b[0m\u001b[31m has bounds that extend       \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31mexactly to simulation edges. This can cause unexpected behavior. If\u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31mintending to extend the structure to infinity along one dimension, \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31muse td.inf as a size variable instead to make this explicit.       \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><span style=\"color: #800000; text-decoration-color: #800000\">WARNING: Structure at </span><span style=\"color: #008000; text-decoration-color: #008000\">'structures[0]'</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>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING: Structure at \u001b[0m\u001b[32m'structures\u001b[0m\u001b[32m[\u001b[0m\u001b[32m0\u001b[0m\u001b[32m]\u001b[0m\u001b[32m'\u001b[0m\u001b[31m has bounds that extend       \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31mexactly to simulation edges. This can cause unexpected behavior. If\u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31mintending to extend the structure to infinity along one dimension, \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31muse td.inf as a size variable instead to make this explicit.       \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\">22:15:53 EST </span><span style=\"color: #800000; text-decoration-color: #800000\">WARNING: Structure at </span><span style=\"color: #008000; text-decoration-color: #008000\">'structures[0]'</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>\n"
      ],
      "text/plain": [
       "\u001b[2;36m22:15:53 EST\u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING: Structure at \u001b[0m\u001b[32m'structures\u001b[0m\u001b[32m[\u001b[0m\u001b[32m0\u001b[0m\u001b[32m]\u001b[0m\u001b[32m'\u001b[0m\u001b[31m has bounds that extend       \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31mexactly to simulation edges. This can cause unexpected behavior. If\u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31mintending to extend the structure to infinity along one dimension, \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31muse td.inf as a size variable instead to make this explicit.       \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><span style=\"color: #800000; text-decoration-color: #800000\">WARNING: Structure at </span><span style=\"color: #008000; text-decoration-color: #008000\">'structures[0]'</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>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING: Structure at \u001b[0m\u001b[32m'structures\u001b[0m\u001b[32m[\u001b[0m\u001b[32m0\u001b[0m\u001b[32m]\u001b[0m\u001b[32m'\u001b[0m\u001b[31m has bounds that extend       \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31mexactly to simulation edges. This can cause unexpected behavior. If\u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31mintending to extend the structure to infinity along one dimension, \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31muse td.inf as a size variable instead to make this explicit.       \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><span style=\"color: #800000; text-decoration-color: #800000\">WARNING: Warning messages were found in the solver log. For more   </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">information, check </span><span style=\"color: #008000; text-decoration-color: #008000\">'SimulationData.log'</span><span style=\"color: #800000; text-decoration-color: #800000\"> or use                     </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'web.download_log(task_id)'</span><span style=\"color: #800000; text-decoration-color: #800000\">.                                       </span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING: Warning messages were found in the solver log. For more   \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31minformation, check \u001b[0m\u001b[32m'SimulationData.log'\u001b[0m\u001b[31m or use                     \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[32m'web.download_log\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m\u001b[31m.                                       \u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "heated core 5.24 K/mW, other arm 2.9 %: Pπ = 25.8 mW\n"
     ]
    }
   ],
   "source": [
    "def heat_sim(length, gap=1.5, arm=14.0, power=1e-3):\n",
    "    \"\"\"Steady 2D heat: two arms `arm` apart, the left heater at `power` (W) over `length` (µm); substrate at 300 K.\"\"\"\n",
    "    solid = lambda name, k, cp, rho: td.MultiPhysicsMedium(\n",
    "        name=name, heat=td.SolidMedium.from_si_units(conductivity=k, capacity=cp, density=rho))\n",
    "    si, ox, tin = solid(\"Si\", 148, 711, 2330), solid(\"SiO2\", 1.38, 709, 2203), solid(\"TiN\", 28, 598, 5240)\n",
    "    box = lambda x0, z0, x1, z1, medium, name: td.Structure(\n",
    "        geometry=td.Box.from_bounds((x0, -td.inf, z0), (x1, td.inf, z1)), medium=medium, name=name)\n",
    "    z_h = T_SI + gap\n",
    "    z_top, z_lo = z_h + 1.12, -22.0  # oxide top (1 µm over the heater); substrate bottom (2 µm BOX, 20 µm Si)\n",
    "    structures = [box(-td.inf, z_lo, td.inf, -2.0, si, \"substrate\"), box(-td.inf, -2.0, td.inf, z_top, ox, \"oxide\")]\n",
    "    for k, x in enumerate((-arm / 2, arm / 2)):\n",
    "        structures += [box(x - 1.0, 0, x + 1.0, T_SI, si, f\"core{k}\"), box(x - 1.5, z_h, x + 1.5, z_h + 0.12, tin, f\"heater{k}\")]\n",
    "    return td.HeatChargeSimulation(\n",
    "        medium=td.MultiPhysicsMedium(name=\"air\", heat=td.FluidMedium()), structures=structures,\n",
    "        center=(0, 0, (z_lo + z_top + 2) / 2), size=(100, 0, z_top + 2 - z_lo),\n",
    "        boundary_spec=[td.HeatChargeBoundarySpec(placement=td.SimulationBoundary(surfaces=[\"z-\"]),\n",
    "                                                 condition=td.TemperatureBC(temperature=300))],  # all else adiabatic\n",
    "        sources=[td.HeatSource(rate=power / (3.0 * 0.12 * length), structures=[\"heater0\"])],  # W/µm³\n",
    "        monitors=[td.TemperatureMonitor(size=(td.inf, 0, td.inf), name=\"T\")],\n",
    "        grid_spec=td.DistanceUnstructuredGrid(dl_interface=0.03, dl_bulk=1.0, distance_interface=0.3, distance_bulk=3.0,\n",
    "                                              non_refined_structures=[\"substrate\"], relative_min_dl=0))\n",
    "\n",
    "\n",
    "temp = web.run(heat_sim(L_PS), task_name=\"ocs_heater\", path=\"data/heater.hdf5\", verbose=False)[\"T\"].temperature\n",
    "core_dT = [float(temp.interp(x=np.linspace(x - 0.95, x + 0.95, 21), y=0.0, z=np.linspace(0.02, T_SI - 0.02, 5)).mean()) - 300\n",
    "           for x in (-7.0, 7.0)]\n",
    "eta, xi = core_dT[0], core_dT[1] / core_dT[0]  # K/mW on the heated core, and the share on the other arm\n",
    "P_PI = LDA0 / (2 * L_PS * DN_DT[0]) / (eta * (1 - xi))\n",
    "print(f\"heated core {eta:.2f} K/mW, other arm {100 * xi:.1f} %: Pπ = {P_PI:.1f} mW\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80b64401",
   "metadata": {},
   "source": [
    "**Off and On states.** Off: no heating, the light crosses (I1 → O2). On: Pπ in the top heater (and 2.9 % of its\n",
    "heat in the bottom arm) sends it to the bar port (I1 → O1)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "21ff6e5f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-02T03:15:53.593060Z",
     "iopub.status.busy": "2026-11-02T03:15:53.592998Z",
     "iopub.status.idle": "2026-11-02T03:15:54.410995Z",
     "shell.execute_reply": "2026-11-02T03:15:54.410622Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Off: loss 0.10 dB at 1550 nm, 0.26 dB worst; ER 40.6 dB at 1550 nm, 32.7 dB worst\n",
      "On: loss 0.11 dB at 1550 nm, 0.34 dB worst; ER 58.3 dB at 1550 nm, 42.9 dB worst\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1100x330 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "off = mzs(split, phase_shifter(length=L_PS), phase_shifter(length=L_PS))\n",
    "on = mzs(split, phase_shifter(length=L_PS, temperature=T0 + eta * P_PI),\n",
    "         phase_shifter(length=L_PS, temperature=T0 + eta * xi * P_PI))\n",
    "t_off, t_on = transmission(off), transmission(on)\n",
    "fig, ax = plt.subplots(1, 2, figsize=(10, 3), width_ratios=[1.3, 1])\n",
    "show_layout(off, ax[0])\n",
    "for t, state, color in ((t_off, \"Off\", \"C0\"), (t_on, \"On\", \"C1\")):\n",
    "    for port, ls in ((\"O2\", \"-\"), (\"O1\", \"--\")):\n",
    "        ax[1].plot(WL * 1e3, db(t[(\"I1@0\", f\"{port}@0\")]), color=color, ls=ls, label=f\"{state}: I1 → {port}\")\n",
    "    through, leak = ((\"O2\", \"O1\") if state == \"Off\" else (\"O1\", \"O2\"))\n",
    "    il, er = -db(t[(\"I1@0\", f\"{through}@0\")]), db(t[(\"I1@0\", f\"{through}@0\")]) - db(t[(\"I1@0\", f\"{leak}@0\")])\n",
    "    print(f\"{state}: loss {il[I0]:.2f} dB at 1550 nm, {il.max():.2f} dB worst; ER {er[I0]:.1f} dB at 1550 nm, {er.min():.1f} dB worst\")\n",
    "ax[1].set(xlabel=\"Wavelength (nm)\", ylabel=\"Transmission (dB)\", ylim=(-80, 3))\n",
    "ax[1].legend(fontsize=7, ncols=2, loc=\"lower center\")\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98b6b932",
   "metadata": {},
   "source": [
    "## 4. No calibration: Monte Carlo\n",
    "Each switch gets one random arm-to-arm width mismatch δw, so its zero-power phase error is δφ = π·S·δw. The arm\n",
    "sensitivity S adds two S-bends (FDTD, from the ±5 nm runs: wide bends are not adiabatic, so FDTD gives more than\n",
    "local modes would) and the straight 2 µm sections (mode solver). The two σ_δw values are estimated from the phase\n",
    "spreads Song et al. measured on fabricated switches (Photonics Research 10, 793, 2022)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "cb71b7e3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-02T03:15:54.412041Z",
     "iopub.status.busy": "2026-11-02T03:15:54.411974Z",
     "iopub.status.idle": "2026-11-02T03:20:02.210730Z",
     "shell.execute_reply": "2026-11-02T03:20:02.210323Z"
    },
    "lines_to_next_cell": 1
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "arm sensitivity 0.0053π/nm (S-bends 0.0041π/nm)\n"
     ]
    },
    {
     "data": {
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       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>at                                                                 \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=mo-effafccf-0dcf-4a4c-8b91-a4882220f9c1\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'https://tidy3d.simulation.cloud/workbench?taskId=mo-effafccf-0dcf-</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=mo-effafccf-0dcf-4a4c-8b91-a4882220f9c1\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">4a4c-8b91-a4882220f9c1'</span></a>.                                           \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m22:15:54 EST\u001b[0m\u001b[2;36m \u001b[0mLoading simulation from local cache. View cached task using web UI \n",
       "\u001b[2;36m             \u001b[0mat                                                                 \n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=2384523;https://tidy3d.simulation.cloud/workbench?taskId=mo-effafccf-0dcf-4a4c-8b91-a4882220f9c1\u001b\\\u001b[32m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=2384524;https://tidy3d.simulation.cloud/workbench?taskId=mo-effafccf-0dcf-4a4c-8b91-a4882220f9c1\u001b\\\u001b[32mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=2384523;https://tidy3d.simulation.cloud/workbench?taskId=mo-effafccf-0dcf-4a4c-8b91-a4882220f9c1\u001b\\\u001b[32m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=2384525;https://tidy3d.simulation.cloud/workbench?taskId=mo-effafccf-0dcf-4a4c-8b91-a4882220f9c1\u001b\\\u001b[32mmo\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=2384523;https://tidy3d.simulation.cloud/workbench?taskId=mo-effafccf-0dcf-4a4c-8b91-a4882220f9c1\u001b\\\u001b[32m-effafccf-0dcf-\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=2384523;https://tidy3d.simulation.cloud/workbench?taskId=mo-effafccf-0dcf-4a4c-8b91-a4882220f9c1\u001b\\\u001b[32m4a4c-8b91-a4882220f9c1'\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\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Loading simulation from local cache. View cached task using web UI \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>at                                                                 \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=mo-a9e9fd34-be7b-4461-b698-647b13ea584e\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'https://tidy3d.simulation.cloud/workbench?taskId=mo-a9e9fd34-be7b-</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=mo-a9e9fd34-be7b-4461-b698-647b13ea584e\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">4461-b698-647b13ea584e'</span></a>.                                           \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mLoading simulation from local cache. View cached task using web UI \n",
       "\u001b[2;36m             \u001b[0mat                                                                 \n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=2384529;https://tidy3d.simulation.cloud/workbench?taskId=mo-a9e9fd34-be7b-4461-b698-647b13ea584e\u001b\\\u001b[32m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=2384530;https://tidy3d.simulation.cloud/workbench?taskId=mo-a9e9fd34-be7b-4461-b698-647b13ea584e\u001b\\\u001b[32mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=2384529;https://tidy3d.simulation.cloud/workbench?taskId=mo-a9e9fd34-be7b-4461-b698-647b13ea584e\u001b\\\u001b[32m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=2384531;https://tidy3d.simulation.cloud/workbench?taskId=mo-a9e9fd34-be7b-4461-b698-647b13ea584e\u001b\\\u001b[32mmo\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=2384529;https://tidy3d.simulation.cloud/workbench?taskId=mo-a9e9fd34-be7b-4461-b698-647b13ea584e\u001b\\\u001b[32m-a9e9fd34-be7b-\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=2384529;https://tidy3d.simulation.cloud/workbench?taskId=mo-a9e9fd34-be7b-4461-b698-647b13ea584e\u001b\\\u001b[32m4461-b698-647b13ea584e'\u001b[0m\u001b]8;;\u001b\\.                                           \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "σ_δw 3.69 nm: median zero-power ER at 1550 nm 33.9 dB, 10–90 % 26.2–39.8 dB\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "σ_δw 1.45 nm: median zero-power ER at 1550 nm 37.6 dB, 10–90 % 33.4–40.5 dB\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 495x330 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "s21 = lambda k: S[k].elements[(\"P0@0\", \"P1@0\")]\n",
    "S_BEND = (np.angle(s21(\"bend+5\") / s21(\"bend\")) + np.angle(s21(\"bend\") / s21(\"bend-5\")))[I0] / np.pi / 20  # π/nm\n",
    "S_ARM = 2 * S_BEND + 2 / LDA0 * dndw[k2] * (L_PS + 3.0)  # + phase shifter and the two 1.5 µm MMI access guides\n",
    "K0L = 2 * np.pi / LDA0 * L_PS\n",
    "SIGMAS = (3.69, 1.45)  # nm\n",
    "print(f\"arm sensitivity {S_ARM:.4f}π/nm (S-bends {2 * S_BEND:.4f}π/nm)\")\n",
    "\n",
    "rng = np.random.default_rng(11)\n",
    "fig, ax = plt.subplots(figsize=(4.5, 3))\n",
    "for sig in SIGMAS:\n",
    "    er = []\n",
    "    for x in np.pi * S_ARM * rng.normal(0, sig, 400):\n",
    "        t = transmission(mzs(split, phase_shifter(length=L_PS, dn=x / K0L), phase_shifter(length=L_PS), pads=False), [F0])\n",
    "        er.append(float((db(t[(\"I1@0\", \"O2@0\")]) - db(t[(\"I1@0\", \"O1@0\")]))[0]))\n",
    "    ax.hist(er, bins=np.arange(16, 46), histtype=\"step\", lw=1.3, label=f\"σ$_{{δw}}$ = {sig} nm\")\n",
    "    print(f\"σ_δw {sig} nm: median zero-power ER at 1550 nm {np.median(er):.1f} dB, \"\n",
    "          f\"10–90 % {np.percentile(er, 10):.1f}–{np.percentile(er, 90):.1f} dB\")\n",
    "ax.set(xlabel=\"Zero-power ER at 1550 nm (dB)\", ylabel=\"Switches (of 400)\")\n",
    "ax.legend(loc=\"upper left\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "279d78cf",
   "metadata": {},
   "source": [
    "## 5. The 4×4 Beneš fabric\n",
    "Six switches in three stages, joined by two perfect shuffles. Every switch port gets the 2 → 0.5 µm taper; links\n",
    "are 0.5 µm guides with 10 µm bends (analytic model, 2 dB/cm), and each shuffle has one crossing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ba67c4b5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-02T03:20:02.211966Z",
     "iopub.status.busy": "2026-11-02T03:20:02.211885Z",
     "iopub.status.idle": "2026-11-02T03:20:04.515651Z",
     "shell.execute_reply": "2026-11-02T03:20:04.515223Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "netlist: 48 physical connections, 0 virtual, 8 external ports; footprint 1323 × 354 µm; layout written to ocs_4x4.gds\n"
     ]
    },
    {
     "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\">22:20:02 EST </span>Loading simulation from local cache. View cached task using web UI \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>at                                                                 \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=mo-c99b9915-e4f4-4492-b858-a4d588ec7aac\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'https://tidy3d.simulation.cloud/workbench?taskId=mo-c99b9915-e4f4-</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=mo-c99b9915-e4f4-4492-b858-a4d588ec7aac\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">4492-b858-a4d588ec7aac'</span></a>.                                           \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m22:20:02 EST\u001b[0m\u001b[2;36m \u001b[0mLoading simulation from local cache. View cached task using web UI \n",
       "\u001b[2;36m             \u001b[0mat                                                                 \n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=2384535;https://tidy3d.simulation.cloud/workbench?taskId=mo-c99b9915-e4f4-4492-b858-a4d588ec7aac\u001b\\\u001b[32m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=2384536;https://tidy3d.simulation.cloud/workbench?taskId=mo-c99b9915-e4f4-4492-b858-a4d588ec7aac\u001b\\\u001b[32mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=2384535;https://tidy3d.simulation.cloud/workbench?taskId=mo-c99b9915-e4f4-4492-b858-a4d588ec7aac\u001b\\\u001b[32m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=2384537;https://tidy3d.simulation.cloud/workbench?taskId=mo-c99b9915-e4f4-4492-b858-a4d588ec7aac\u001b\\\u001b[32mmo\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=2384535;https://tidy3d.simulation.cloud/workbench?taskId=mo-c99b9915-e4f4-4492-b858-a4d588ec7aac\u001b\\\u001b[32m-c99b9915-e4f4-\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=2384535;https://tidy3d.simulation.cloud/workbench?taskId=mo-c99b9915-e4f4-4492-b858-a4d588ec7aac\u001b\\\u001b[32m4492-b858-a4d588ec7aac'\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\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Loading simulation from local cache. View cached task using web UI \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>at                                                                 \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=mo-13d1e2ec-0775-4d5c-a0ef-499ef32036db\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'https://tidy3d.simulation.cloud/workbench?taskId=mo-13d1e2ec-0775-</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=mo-13d1e2ec-0775-4d5c-a0ef-499ef32036db\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">4d5c-a0ef-499ef32036db'</span></a>.                                           \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mLoading simulation from local cache. View cached task using web UI \n",
       "\u001b[2;36m             \u001b[0mat                                                                 \n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=2384541;https://tidy3d.simulation.cloud/workbench?taskId=mo-13d1e2ec-0775-4d5c-a0ef-499ef32036db\u001b\\\u001b[32m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=2384542;https://tidy3d.simulation.cloud/workbench?taskId=mo-13d1e2ec-0775-4d5c-a0ef-499ef32036db\u001b\\\u001b[32mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=2384541;https://tidy3d.simulation.cloud/workbench?taskId=mo-13d1e2ec-0775-4d5c-a0ef-499ef32036db\u001b\\\u001b[32m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=2384543;https://tidy3d.simulation.cloud/workbench?taskId=mo-13d1e2ec-0775-4d5c-a0ef-499ef32036db\u001b\\\u001b[32mmo\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=2384541;https://tidy3d.simulation.cloud/workbench?taskId=mo-13d1e2ec-0775-4d5c-a0ef-499ef32036db\u001b\\\u001b[32m-13d1e2ec-0775-\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=2384541;https://tidy3d.simulation.cloud/workbench?taskId=mo-13d1e2ec-0775-4d5c-a0ef-499ef32036db\u001b\\\u001b[32m4d5c-a0ef-499ef32036db'\u001b[0m\u001b]8;;\u001b\\.                                           \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "all-cross: In1–In4 → Out[3, 4, 1, 2]; loss 0.39–0.40 dB at 1550 nm, 0.94 dB worst; ER 36.1 dB at 1550 nm, 28.8 dB worst\n",
      "all-bar: In1–In4 → Out[1, 2, 3, 4]; loss 0.35–0.51 dB at 1550 nm, 1.16 dB worst; ER 40.6 dB at 1550 nm, 35.1 dB worst\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1210x726 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "@pf.parametric_component\n",
    "def route(*, points=((0.0, 0.0), (10.0, 0.0)), radius=10.0):\n",
    "    \"\"\"0.5 µm guide through axis-aligned corner points with 90° circular bends.\"\"\"\n",
    "    tech = pf.config.default_technology\n",
    "    pts = np.array(points, float)\n",
    "    d = [(q - p) / np.hypot(*(q - p)) for p, q in zip(pts[:-1], pts[1:])]\n",
    "    path = pf.Path(tuple(pts[0]), width=0.5)\n",
    "    for k in range(1, len(pts) - 1):\n",
    "        path.segment(tuple(pts[k] - radius * d[k - 1]))\n",
    "        path.turn(90 if d[k - 1][0] * d[k][1] - d[k - 1][1] * d[k][0] > 0 else -90, radius=radius, euler_fraction=0)\n",
    "    path.segment(tuple(pts[-1]))\n",
    "    c = pf.Component(\"Route\", technology=tech)\n",
    "    c.add(\"Si\", path)\n",
    "    angle = lambda v: float(np.degrees(np.arctan2(v[1], v[0])))\n",
    "    c.add_port(pf.Port(tuple(pts[0]), angle(d[0]), tech.ports[\"WG500\"]), port_name=\"P0\")\n",
    "    c.add_port(pf.Port(tuple(pts[-1]), angle(-d[-1]), tech.ports[\"WG500\"]), port_name=\"P1\")\n",
    "    c.add_model(pf.AnalyticWaveguideModel(n_eff=float(N_ROUTE[0]), n_group=float(NG_ROUTE[0]), length=path.length(),\n",
    "                                          propagation_loss=2e-4, reference_frequency=F0), \"Analytic\")\n",
    "    return c\n",
    "\n",
    "\n",
    "def benes(cells, pitch_y=200.0, gap_x=120.0, dx=30.0):\n",
    "    \"\"\"{(stage, row): switch} with row 0 on top, joined by perfect shuffles. In each shuffle one diagonal link runs\n",
    "    vertically and the other horizontally through a single crossing. All-bar routes In_k → Out_k.\"\"\"\n",
    "    c = pf.Component(\"4x4 Benes\")\n",
    "    (x0, _), (x1, _) = cells[(0, 0)].bounds()\n",
    "    pitch_x = x1 - x0 + gap_x\n",
    "    refs = {k: c.add_reference(pf.Reference(cell, origin=(k[0] * pitch_x, (0.5 - k[1]) * pitch_y))) for k, cell in cells.items()}\n",
    "    tips = {}  # narrow end of the taper on every switch port\n",
    "    for k, ref in refs.items():\n",
    "        for p in (\"I1\", \"I2\", \"O1\", \"O2\"):\n",
    "            t = c.add_reference(taper)\n",
    "            t.connect(\"P0\", ref[p])\n",
    "            tips[(k, p)] = t[\"P1\"]\n",
    "    end = {k: np.array(port.center) for k, port in tips.items()}\n",
    "    def link(*pts):\n",
    "        r = route(points=tuple(tuple(float(v) for v in q) for q in pts))\n",
    "        r.name = f\"Route {sum(ref.component.name.startswith('Route') for ref in c.references) + 1}\"  # unique GDS cell names\n",
    "        c.add_reference(r)\n",
    "\n",
    "    arm = cross.ports[\"P2\"].center[0]  # crossing port distance from its centre\n",
    "    for s in (0, 1):\n",
    "        a0, a1, b0, b1 = (s, 0), (s, 1), (s + 1, 0), (s + 1, 1)\n",
    "        link(end[(a0, \"O1\")], end[(b0, \"I1\")])\n",
    "        link(end[(a1, \"O2\")], end[(b1, \"I2\")])\n",
    "        o2, i1, o1, i2 = end[(a0, \"O2\")], end[(b1, \"I1\")], end[(a1, \"O1\")], end[(b0, \"I2\")]\n",
    "        xc = 0.5 * (o2[0] + i1[0])\n",
    "        c.add_reference(pf.Reference(cross, origin=(xc, 0.0)))\n",
    "        link(o2, (xc, o2[1]), (xc, arm))                                   # vertical path through the crossing\n",
    "        link((xc, -arm), (xc, i1[1]), i1)\n",
    "        link(o1, (xc - dx, o1[1]), (xc - dx, 0.0), (xc - arm, 0.0))       # horizontal path\n",
    "        link((xc + arm, 0.0), (xc + dx, 0.0), (xc + dx, i2[1]), i2)\n",
    "    for n, (k, p) in enumerate((((0, 0), \"I1\"), ((0, 0), \"I2\"), ((0, 1), \"I1\"), ((0, 1), \"I2\"))):\n",
    "        c.add_port(tips[(k, p)], port_name=f\"In{n + 1}\")\n",
    "    for n, (k, p) in enumerate((((2, 0), \"O1\"), ((2, 0), \"O2\"), ((2, 1), \"O1\"), ((2, 1), \"O2\"))):\n",
    "        c.add_port(tips[(k, p)], port_name=f\"Out{n + 1}\")\n",
    "    c.add_model(pf.CircuitModel(), \"Circuit\")\n",
    "    return c\n",
    "\n",
    "\n",
    "def cell(bar=False, dphi=0.0, name=\"2x2 switch\"):\n",
    "    \"\"\"One switch of the fabric: heated to Pπ for bar, with a zero-power arm phase error dphi (rad).\"\"\"\n",
    "    p = P_PI if bar else 0.0\n",
    "    return mzs(split, phase_shifter(length=L_PS, temperature=T0 + eta * p, dn=dphi / K0L),\n",
    "               phase_shifter(length=L_PS, temperature=T0 + eta * xi * p), name=name)\n",
    "\n",
    "\n",
    "STAGES = [(0, 0), (0, 1), (1, 0), (1, 1), (2, 0), (2, 1)]  # SW1..SW6\n",
    "fabric = lambda state, dphis=(0.0,) * 6: benes({k: cell(bool(b), d, f\"SW{n + 1}\")\n",
    "                                               for n, (k, b, d) in enumerate(zip(STAGES, state, dphis))})\n",
    "power = lambda t: np.array([[abs(t[(f\"In{i}@0\", f\"Out{j}@0\")]) ** 2 for j in range(1, 5)] for i in range(1, 5)])  # [in, out, f]\n",
    "\n",
    "layout = fabric((0,) * 6)\n",
    "layout.write_gds(\"ocs_4x4.gds\")\n",
    "nl = layout.get_netlist()\n",
    "w, h = np.ptp(np.array(layout.bounds()), axis=0)\n",
    "print(f\"netlist: {len(nl['connections'])} physical connections, {len(nl['virtual connections'])} virtual, \"\n",
    "      f\"{len(layout.ports)} external ports; footprint {w:.0f} × {h:.0f} µm; layout written to ocs_4x4.gds\")\n",
    "\n",
    "T4 = {\"all-cross\": power(transmission(layout)), \"all-bar\": power(transmission(fabric((1,) * 6)))}\n",
    "for name, t in T4.items():\n",
    "    main = t[..., I0].argmax(1)\n",
    "    il = np.array([-10 * np.log10(t[i, main[i]]) for i in range(4)])\n",
    "    er = np.array([10 * np.log10(t[i, main[i]] / np.delete(t[:, main[i]], i, axis=0).max(0)) for i in range(4)])\n",
    "    print(f\"{name}: In1–In4 → Out{(main + 1).tolist()}; loss {il[:, I0].min():.2f}–{il[:, I0].max():.2f} dB at 1550 nm, \"\n",
    "          f\"{il.max():.2f} dB worst; ER {er[:, I0].min():.1f} dB at 1550 nm, {er.min():.1f} dB worst\")\n",
    "\n",
    "fig = plt.figure(figsize=(11, 6.6))\n",
    "top, bottom = fig.subfigures(2, 1)\n",
    "show_layout(layout, top.subplots())\n",
    "ax = bottom.subplots(1, 3, width_ratios=[0.9, 1, 1])\n",
    "fd = runs[\"cross\"].runners[\"P0@0\"].data[\"field\"]\n",
    "E = np.sqrt(sum(abs(fd.field_components[q].isel(f=0).squeeze()) ** 2 for q in (\"Ex\", \"Ey\", \"Ez\")))\n",
    "ax[0].pcolormesh(E.x, E.y, (E / E.max()).T, cmap=\"magma\", shading=\"auto\", rasterized=True)\n",
    "ax[0].set(xlim=(-4.8, 4.8), ylim=(-4.8, 4.8), aspect=\"equal\", xlabel=\"x (µm)\", ylabel=\"y (µm)\", title=\"Crossing: |E| at 1550 nm\")\n",
    "for a, (name, t) in zip(ax[1:], T4.items()):\n",
    "    t = 10 * np.log10(t[..., I0])\n",
    "    a.imshow(t, cmap=\"viridis\", vmin=-60, vmax=0)\n",
    "    for i, j in np.ndindex(4, 4):\n",
    "        a.text(j, i, \"< -80\" if t[i, j] < -80 else f\"{t[i, j]:.1f}\", ha=\"center\", va=\"center\", fontsize=8,\n",
    "               color=\"k\" if t[i, j] > -25 else \"w\")\n",
    "    a.set(xticks=range(4), xticklabels=[f\"O{j + 1}\" for j in range(4)], yticks=range(4),\n",
    "          yticklabels=[f\"I{i + 1}\" for i in range(4)], title=f\"{name} (dB, 1550 nm)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "658acd9e",
   "metadata": {},
   "source": [
    "**No calibration, 4×4.** Every switch gets its own random phase error; all heaters off (all-cross), 1550 nm. The\n",
    "figure of merit is the worst of the four outputs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f81709b6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-02T03:20:04.516745Z",
     "iopub.status.busy": "2026-11-02T03:20:04.516660Z",
     "iopub.status.idle": "2026-11-02T03:22:55.413530Z",
     "shell.execute_reply": "2026-11-02T03:22:55.412653Z"
    }
   },
   "outputs": [
    {
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       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>at                                                                 \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=mo-bdbf4233-f260-4271-9289-13357e3e95b5\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'https://tidy3d.simulation.cloud/workbench?taskId=mo-bdbf4233-f260-</span></a>\n",
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       "\u001b[2;36m22:20:04 EST\u001b[0m\u001b[2;36m \u001b[0mLoading simulation from local cache. View cached task using web UI \n",
       "\u001b[2;36m             \u001b[0mat                                                                 \n",
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       "\u001b[2;36m             \u001b[0m\u001b]8;id=2384547;https://tidy3d.simulation.cloud/workbench?taskId=mo-bdbf4233-f260-4271-9289-13357e3e95b5\u001b\\\u001b[32m4271-9289-13357e3e95b5'\u001b[0m\u001b]8;;\u001b\\.                                           \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\">             </span>Loading simulation from local cache. View cached task using web UI \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>at                                                                 \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=mo-499b04b5-0dd7-4373-959c-179f7485fcef\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'https://tidy3d.simulation.cloud/workbench?taskId=mo-499b04b5-0dd7-</span></a>\n",
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       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mLoading simulation from local cache. View cached task using web UI \n",
       "\u001b[2;36m             \u001b[0mat                                                                 \n",
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     "output_type": "stream",
     "text": [
      "σ_δw 3.69 nm: worst-output ER at 1550 nm, median 26.0 dB (10–90 % 21.4–30.8 dB)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "σ_δw 1.45 nm: worst-output ER at 1550 nm, median 32.1 dB (10–90 % 28.3–34.7 dB)\n"
     ]
    }
   ],
   "source": [
    "rng = np.random.default_rng(7)\n",
    "main = T4[\"all-cross\"][..., I0].argmax(1)\n",
    "for sig in SIGMAS:\n",
    "    worst = []\n",
    "    for _ in range(150):\n",
    "        t = power(transmission(fabric((0,) * 6, np.pi * S_ARM * rng.normal(0, sig, 6)), [F0]))[..., 0]\n",
    "        worst.append(min(10 * np.log10(t[i, main[i]] / np.delete(t[:, main[i]], i).max()) for i in range(4)))\n",
    "    print(f\"σ_δw {sig} nm: worst-output ER at 1550 nm, median {np.median(worst):.1f} dB \"\n",
    "          f\"(10–90 % {np.percentile(worst, 10):.1f}–{np.percentile(worst, 90):.1f} dB)\")"
   ]
  }
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