{
 "cells": [
  {
   "cell_type": "raw",
   "id": "23a1b90d",
   "metadata": {
    "tags": [
     "remove-cell"
    ]
   },
   "source": [
    "/// script\n",
    "requires-python = \">=3.12,<3.13\"\n",
    "dependencies = [\n",
    "  \"qpdk[models] @ git+https://github.com/gdsfactory/quantum-rf-pdk.git\",\n",
    "  \"gsim @ git+https://github.com/nikosavola/gsim.git@946c3973b6311f0a4a3cf3bd601336ad7a186dfb\",\n",
    "]\n",
    "///"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab15ed51",
   "metadata": {},
   "source": [
    "# Palace Eigenmode Simulation of a Transmon with a Readout Resonator\n",
    "\n",
    "This notebook demonstrates a full-wave eigenmode simulation of a **double-pad\n",
    "transmon qubit** capacitively coupled to a **quarter-wave CPW readout\n",
    "resonator** and read through a **probeline**, using\n",
    "[Palace](https://awslabs.github.io/palace/), an open-source parallel 3-D\n",
    "finite-element electromagnetic solver, driven through\n",
    "[gsim](https://gdsfactory.github.io/gsim/). It is the open-source counterpart\n",
    "of the {doc}`hfss_eigenmode_resonator` notebook: same physics, no commercial\n",
    "solver license.\n",
    "\n",
    "The workflow goes from layout to nearby qubit and readout eigenmodes, with an\n",
    "analytical CPW estimate as a cross-check:\n",
    "\n",
    "````{only} html\n",
    "```{mermaid}\n",
    "flowchart TB\n",
    "    A[\"Layout<br>(qpdk cells)\"]\n",
    "    B[\"FEM regions<br>(subtractive mask to explicit conductor)\"]\n",
    "    C[\"Analytical estimate<br>(CPW cross-section)\"]\n",
    "    D[\"Eigenmode setup and mesh<br>(gsim, Gmsh)\"]\n",
    "    E[\"Palace solve<br>(cluster)\"]\n",
    "    F[\"Eigenmodes vs. estimate\"]\n",
    "    G[\"Check participation, fields, and mesh sensitivity\"]\n",
    "    A --> B --> D --> E --> F --> G\n",
    "    C --> F\n",
    "```\n",
    "````\n",
    "\n",
    "Eigenmode analysis is the workhorse behind quantitative qubit design: the\n",
    "linearized eigenfrequencies and eigenfields of the Josephson circuit, with the\n",
    "junction treated as a lumped inductor, determine the system Hamiltonian once\n",
    "completed with the junction's energy participation\n",
    "{cite:p}`minevEnergyParticipationQuantization2021,niggBlackboxSuperconductingCircuit2012`.\n",
    "Here we stop at the classical part: eigenfrequencies and quality factors.\n",
    "\n",
    "::::{admonition} Required extras\n",
    ":class: tip\n",
    "\n",
    "This notebook needs the `models` extra:\n",
    "\n",
    "```bash\n",
    "uv add \"qpdk[models]\"\n",
    "# or, from a checkout of this repository:\n",
    "uv sync --extra models --group palace\n",
    "# or with pip:\n",
    "pip install \"qpdk[models]\"\n",
    "```\n",
    "\n",
    "The layout and setup cells also need [gsim](https://gdsfactory.github.io/gsim/).\n",
    "The `palace` dependency group installs the pinned version used here. The\n",
    "standalone script and Colab setup install that same revision. Palace itself\n",
    "is an external executable. Its mesh and solve calls are fenced examples,\n",
    "and the saved results let the analysis run without launching a solver.\n",
    "\n",
    "See the {ref}`extras reference <notebook-extras>` for what each extra installs.\n",
    "::::\n",
    "\n",
    "The results shown below were produced with Palace on an HPC cluster; the\n",
    "solver data is embedded in hidden cells so the rendered docs show the\n",
    "analysis, not the raw tables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3b272e41",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-27T13:15:13.162032Z",
     "iopub.status.busy": "2026-10-27T13:15:13.161937Z",
     "iopub.status.idle": "2026-10-27T13:15:13.164629Z",
     "shell.execute_reply": "2026-10-27T13:15:13.164105Z"
    },
    "tags": [
     "hide-input",
     "hide-output"
    ]
   },
   "outputs": [],
   "source": [
    "import importlib\n",
    "import sys\n",
    "from functools import partial\n",
    "from pathlib import Path\n",
    "\n",
    "if \"google.colab\" in sys.modules:\n",
    "    import subprocess\n",
    "\n",
    "    print(\"Running in Google Colab. Installing QPDK...\")\n",
    "    subprocess.check_call([\n",
    "        sys.executable,\n",
    "        \"-m\",\n",
    "        \"pip\",\n",
    "        \"install\",\n",
    "        \"-q\",\n",
    "        \"qpdk[models] @ git+https://github.com/gdsfactory/quantum-rf-pdk.git\",\n",
    "        \"gsim @ git+https://github.com/nikosavola/gsim.git@946c3973b6311f0a4a3cf3bd601336ad7a186dfb\",\n",
    "    ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d3b6343b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-27T13:15:13.165775Z",
     "iopub.status.busy": "2026-10-27T13:15:13.165671Z",
     "iopub.status.idle": "2026-10-27T13:15:13.187823Z",
     "shell.execute_reply": "2026-10-27T13:15:13.187271Z"
    },
    "tags": [
     "hide-input",
     "hide-output"
    ]
   },
   "outputs": [],
   "source": [
    "import gdsfactory as gf\n",
    "import klayout.db as kdb\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib_inline\n",
    "import numpy as np\n",
    "from matplotlib import font_manager\n",
    "from matplotlib.colors import LogNorm\n",
    "from scipy.ndimage import gaussian_filter\n",
    "\n",
    "import qpdk.colormaps  # ruff: ignore[unused-import]\n",
    "from qpdk import PDK, logger\n",
    "from qpdk.cells import (\n",
    "    double_pad_transmon_with_bbox,\n",
    "    plate_capacitor_single,\n",
    "    straight,\n",
    "    transmon_with_resonator_and_probeline,\n",
    ")\n",
    "from qpdk.config import PATH\n",
    "from qpdk.models.constants import c_0\n",
    "from qpdk.models.cpw import cpw_epsilon_eff, cpw_parameters, get_cpw_dimensions\n",
    "from qpdk.models.resonator import resonator_frequency\n",
    "from qpdk.simulation import FEM_LAYERS, to_fem_regions\n",
    "from qpdk.tech import LAYER, material_properties\n",
    "\n",
    "PDK.activate()\n",
    "\n",
    "for style in (PATH.repo / \"docs\" / \"qpdk.mplstyle\", \"qpdk\"):\n",
    "    try:\n",
    "        plt.style.use(style)\n",
    "    except OSError:\n",
    "        continue\n",
    "    break\n",
    "\n",
    "for font_path in (PATH.repo / \"build\" / \"docs-fonts\").glob(\"*\"):\n",
    "    if font_path.suffix.lower() in {\".otf\", \".ttf\"}:\n",
    "        font_manager.fontManager.addfont(str(font_path))\n",
    "\n",
    "installed_fonts = {font.name for font in font_manager.fontManager.ttflist}\n",
    "plt.rcParams[\"font.sans-serif\"] = [\n",
    "    name\n",
    "    for name in (\"Inter\", \"Outfit\", \"DejaVu Sans\", \"Helvetica\", \"Arial\")\n",
    "    if name in installed_fonts\n",
    "] + [\"sans-serif\"]\n",
    "\n",
    "# This notebook ships with its outputs committed, so keep figures raster:\n",
    "# matplotlib's SVG embeds a DOCTYPE URL that link checkers follow, and a dense\n",
    "# field map as SVG is megabytes for no visual gain.\n",
    "matplotlib_inline.backend_inline.set_matplotlib_formats(\"png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "efeda74b",
   "metadata": {},
   "source": [
    "## Simulation layout\n",
    "\n",
    "The device is the\n",
    "{func}`~qpdk.cells.transmon_with_resonator_and_probeline` cell: a double-pad\n",
    "transmon, a meandered quarter-wave CPW resonator coupled to one pad, and a\n",
    "probeline feed coupled along the readout line. It exposes exactly\n",
    "the ports a 3-D solve needs:\n",
    "\n",
    "- ``junction``: the position of the Josephson junction between the two qubit\n",
    "  pads, which becomes the lumped inductive port.\n",
    "- ``coupling_o1`` / ``coupling_o2``: the two ends of the probeline feed, which\n",
    "  become 50 Ω CPW lumped ports.\n",
    "\n",
    "The cell's ``resonator_length`` includes the meander, probeline coupling\n",
    "section, and qubit-side route. The conductor is shorted at the far end of the\n",
    "meander and open at the plate facing the qubit. The route is already in the\n",
    "length used for the quarter-wave estimate below.\n",
    "\n",
    "Two details need care when the cell is used standalone:\n",
    "\n",
    "- The meander's far end must reach ground. The qubit-side plate stays open,\n",
    "  outside the qubit etch, and separated from the pads by a capacitive gap.\n",
    "- Bare probeline port ends touch ground at their end faces, so the wrapper\n",
    "  extends the gap etch past both feed ends.\n",
    "\n",
    "A ``SIM_AREA`` rectangle bounds the silicon and vacuum volumes. The default\n",
    "ground margin is 500 µm, and each probeline end extends another 1000 µm\n",
    "beyond the coupling section. The 150 × 300 µm pads retain a 15 µm junction\n",
    "gap. Junction inductance sets their frequency after the pad size changes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0e942623",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-27T13:15:13.189460Z",
     "iopub.status.busy": "2026-10-27T13:15:13.189364Z",
     "iopub.status.idle": "2026-10-27T13:15:13.464520Z",
     "shell.execute_reply": "2026-10-27T13:15:13.463938Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m2026-10-27 15:15:13.296\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36m<module>\u001b[0m:\u001b[36m119\u001b[0m - \u001b[1mPorts: ['junction', 'o1', 'coupling_o1', 'coupling_o2']\u001b[0m\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m2026-10-27 15:15:13.297\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36m<module>\u001b[0m:\u001b[36m121\u001b[0m - \u001b[1mSimulation footprint: 4020 × 2434 µm\u001b[0m\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# The cell uses this total to size the meander after laying out the separate\n",
    "# coupling section and qubit-side arm.\n",
    "RESONATOR_LENGTH = 7020.0  # µm\n",
    "RESONATOR_MEANDERS = 5\n",
    "RESONATOR_MEANDER_START = (-1100.0, -1200.0)  # µm\n",
    "QUBIT_PAD_SIZE = (150.0, 300.0)  # µm\n",
    "QUBIT_PAD_GAP = 15.0  # µm\n",
    "QUBIT_BBOX_EXTENSION = 40.0  # µm\n",
    "GROUND_MARGIN = 500.0  # µm\n",
    "PROBELINE_EXTENSION = 1000.0  # µm per end\n",
    "JUNCTION_INDUCTANCE = 2.515e-08  # H\n",
    "JUNCTION_PORT_WIDTH = 2.0  # µm\n",
    "JUNCTION_PORT_LENGTH = QUBIT_PAD_GAP\n",
    "NEAR_MESH = 0.55  # µm\n",
    "ELEMENT_ORDER = 3\n",
    "COUPLER_WIDTH = 10.0  # µm\n",
    "COUPLER_LENGTH = 120.0  # µm\n",
    "COUPLER_OFFSET = (-QUBIT_BBOX_EXTENSION - 6.0, 0.0)  # µm\n",
    "COUPLER_ETCH_MARGIN = 2.0  # µm\n",
    "\n",
    "\n",
    "@gf.cell\n",
    "def qubit_resonator_sim_component(\n",
    "    resonator_length: float = RESONATOR_LENGTH,\n",
    "    meanders: int = RESONATOR_MEANDERS,\n",
    "    pad_size: tuple[float, float] = QUBIT_PAD_SIZE,\n",
    "    ground_margin: float = GROUND_MARGIN,\n",
    "    probeline_extension: float = PROBELINE_EXTENSION,\n",
    ") -> gf.Component:\n",
    "    \"\"\"Transmon, resonator and probeline wrapped with a simulation area.\n",
    "\n",
    "    Args:\n",
    "        resonator_length: Layout length budget used to size the meander in µm.\n",
    "        meanders: Number of meander sections.\n",
    "        pad_size: Dimensions of each qubit pad in µm.\n",
    "        ground_margin: Ground clearance around the extended layout in µm.\n",
    "        probeline_extension: Extra CPW length at each feed end in µm.\n",
    "\n",
    "    Returns:\n",
    "        Component with the simulation layout and ports\n",
    "        ``junction``, ``coupling_o1``, ``coupling_o2``.\n",
    "    \"\"\"\n",
    "    c = gf.Component()\n",
    "\n",
    "    ref = c << transmon_with_resonator_and_probeline(\n",
    "        qubit=partial(\n",
    "            double_pad_transmon_with_bbox,\n",
    "            pad_size=pad_size,\n",
    "            pad_gap=QUBIT_PAD_GAP,\n",
    "            bbox_extension=QUBIT_BBOX_EXTENSION,\n",
    "        ),\n",
    "        coupler=partial(\n",
    "            plate_capacitor_single,\n",
    "            width=COUPLER_WIDTH,\n",
    "            length=COUPLER_LENGTH,\n",
    "            etch_bbox_margin=COUPLER_ETCH_MARGIN,\n",
    "        ),\n",
    "        coupler_offset=COUPLER_OFFSET,\n",
    "        resonator_length=resonator_length,\n",
    "        resonator_meanders=meanders,\n",
    "        resonator_meander_start=RESONATOR_MEANDER_START,\n",
    "        # The open end is the plate at the qubit; this bare meander end meets ground.\n",
    "        resonator_open_end=False,\n",
    "        qubit_rotation=90,\n",
    "    )\n",
    "    junction = ref.ports[\"junction\"]\n",
    "    c.add_port(\n",
    "        \"junction\",\n",
    "        center=junction.center,\n",
    "        width=JUNCTION_PORT_WIDTH,\n",
    "        orientation=junction.orientation,\n",
    "        layer=junction.layer,\n",
    "        port_type=junction.port_type,\n",
    "    )\n",
    "    c.add_ports(ref.ports.filter(regex=r\"^(?!junction$|coupling_o[12]$)\"))\n",
    "    routed = ref.cell.info[\"length\"]\n",
    "    assert abs(routed - resonator_length) < 1.0, (\n",
    "        f\"cell routed {routed} µm, expected {resonator_length} µm\"\n",
    "    )\n",
    "    c.info[\"routed_length\"] = routed\n",
    "    meander_ref = next(\n",
    "        inst\n",
    "        for inst in ref.cell.insts\n",
    "        if inst.cell.info.get(\"resonator_type\") == \"quarter_wave\"\n",
    "    )\n",
    "    c.info[\"meander_length\"] = meander_ref.cell.info[\"length\"]\n",
    "    qubit_ref, coupler_ref = list(ref.cell.insts)[:2]\n",
    "    assert (\n",
    "        coupler_ref.dbbox().top - COUPLER_ETCH_MARGIN\n",
    "        < qubit_ref.dbbox().bottom\n",
    "        < coupler_ref.dbbox().top\n",
    "    ), \"open-end metal must stay outside the qubit etch while the etch windows meet\"\n",
    "    assert (\n",
    "        qubit_ref.ports[\"left_pad\"].y - (coupler_ref.dbbox().top - COUPLER_ETCH_MARGIN)\n",
    "        >= 20\n",
    "    ), \"the open end is too close to the qubit pad\"\n",
    "    assert meander_ref.dbbox().right < coupler_ref.ports[\"o1\"].x - 30, (\n",
    "        \"the meander crosses the qubit-side route\"\n",
    "    )\n",
    "    for name in (\"coupling_o1\", \"coupling_o2\"):\n",
    "        port = ref.ports[name]\n",
    "        if probeline_extension:\n",
    "            feed = c << straight(length=probeline_extension, cross_section=\"cpw\")\n",
    "            feed.connect(\"o1\", port)\n",
    "            port = feed.ports[\"o2\"]\n",
    "        c.add_port(name, port=port)\n",
    "        # Leave an etched clearance behind the matched load.\n",
    "        ext = c << straight(length=10.0, cross_section=\"etch\")\n",
    "        ext.connect(\"o1\", port, allow_layer_mismatch=True)\n",
    "\n",
    "    c.kdb_cell.shapes(gf.get_layer(LAYER.SIM_AREA)).insert(\n",
    "        c.bbox().enlarged(ground_margin, ground_margin)\n",
    "    )\n",
    "    return c\n",
    "\n",
    "\n",
    "component = qubit_resonator_sim_component()\n",
    "component.plot()\n",
    "logger.info(f\"Ports: {[p.name for p in component.ports]}\")\n",
    "bounds = component.dbbox()\n",
    "logger.info(f\"Simulation footprint: {bounds.width():.0f} × {bounds.height():.0f} µm\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c2b0740",
   "metadata": {},
   "source": [
    "The close view shows the 10 µm wide readout plate below the qubit's etched\n",
    "box. Its own etch joins that box, but the metal stays outside it and at least\n",
    "20 µm from the qubit pad. The trace continues down to the meander, whose\n",
    "opposite end terminates on the ground plane."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "41e5d91a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-27T13:15:13.465820Z",
     "iopub.status.busy": "2026-10-27T13:15:13.465712Z",
     "iopub.status.idle": "2026-10-27T13:15:13.651023Z",
     "shell.execute_reply": "2026-10-27T13:15:13.650422Z"
    }
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "qubit_close_view = gf.Component()\n",
    "qubit_close_view << component\n",
    "qubit_close_view.trim(left=-230, bottom=-300, right=230, top=250, flatten=True)\n",
    "qubit_close_view.plot(show_labels=False, show_ruler=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9171467d",
   "metadata": {},
   "source": [
    "At the opposite end, the meander etch stops at the trace end. The conductor\n",
    "therefore joins the surrounding ground plane there, providing the short of\n",
    "the quarter-wave resonator."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a145e18f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-27T13:15:13.652288Z",
     "iopub.status.busy": "2026-10-27T13:15:13.652180Z",
     "iopub.status.idle": "2026-10-27T13:15:13.834937Z",
     "shell.execute_reply": "2026-10-27T13:15:13.834282Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "device_ref = next(iter(component.insts))\n",
    "meander_ref = next(\n",
    "    inst\n",
    "    for inst in device_ref.cell.insts\n",
    "    if inst.cell.info.get(\"resonator_type\") == \"quarter_wave\"\n",
    ")\n",
    "short_x, short_y = meander_ref.ports[\"o2\"].center\n",
    "short_close_view = gf.Component()\n",
    "short_close_view << component\n",
    "short_close_view.trim(\n",
    "    left=short_x - 80,\n",
    "    bottom=short_y - 65,\n",
    "    right=short_x + 80,\n",
    "    top=short_y + 65,\n",
    "    flatten=True,\n",
    ")\n",
    "short_close_view.plot(show_labels=False, show_ruler=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4adc5fa2",
   "metadata": {},
   "source": [
    "## From etch layers to simulation regions\n",
    "\n",
    "qpdk draws metal subtractively, but a volumetric solver needs explicit\n",
    "conductor and dielectric bodies; {func}`~qpdk.simulation.to_fem_regions`\n",
    "converts between the two with ``SIM_AREA - (M1_ETCH - M1_DRAW)``.\n",
    "{ref}`subtractive-to-positive` works through the conversion with a picture,\n",
    "including what it does to an additive shape sitting inside an etched gap."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5a44b0b0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-27T13:15:13.836340Z",
     "iopub.status.busy": "2026-10-27T13:15:13.836233Z",
     "iopub.status.idle": "2026-10-27T13:15:14.039852Z",
     "shell.execute_reply": "2026-10-27T13:15:14.039305Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "etched = to_fem_regions(component)\n",
    "metal = kdb.Region(\n",
    "    etched.kdb_cell.begin_shapes_rec(\n",
    "        etched.kdb_cell.layout().layer(*FEM_LAYERS[\"SUPERCONDUCTOR\"])\n",
    "    )\n",
    ").merged()\n",
    "assert len(metal) == 4, \"expected the grounded readout, feed, and two qubit pads\"\n",
    "islands = list(metal)\n",
    "\n",
    "\n",
    "def _metal_island(x: float, y: float) -> int:\n",
    "    point = kdb.Point(round(x / etched.kcl.dbu), round(y / etched.kcl.dbu))\n",
    "    matches = [index for index, polygon in enumerate(islands) if polygon.inside(point)]\n",
    "    assert len(matches) == 1, f\"expected one conductor at ({x}, {y}), got {matches}\"\n",
    "    return matches[0]\n",
    "\n",
    "\n",
    "ground = _metal_island(component.dbbox().left + 5, component.dbbox().top - 5)\n",
    "readout = _metal_island(*component.ports[\"o1\"].center)\n",
    "feed1 = component.ports[\"coupling_o1\"].center\n",
    "feed2 = component.ports[\"coupling_o2\"].center\n",
    "feed = _metal_island((feed1[0] + feed2[0]) / 2, (feed1[1] + feed2[1]) / 2)\n",
    "pad_centres = (QUBIT_PAD_SIZE[0] + QUBIT_PAD_GAP) / 2\n",
    "pad1 = _metal_island(0, -pad_centres)\n",
    "pad2 = _metal_island(0, pad_centres)\n",
    "assert readout == ground, \"the far end of the readout must meet ground\"\n",
    "assert len({readout, feed, pad1, pad2}) == 4, (\n",
    "    \"the readout must stay separate from the feed and both qubit pads\"\n",
    ")\n",
    "\n",
    "# The cell's ``junction`` port is a *placement* marker: it points along the\n",
    "# junction wire, which the transmon cells build perpendicular to the pad-to-pad\n",
    "# axis (``left_pad_inner`` and ``right_pad_inner`` face each other across the\n",
    "# gap, while the junction port sits at the gap centre at 90° to them). A lumped\n",
    "# port has to bridge pad to pad, so rotate by that 90°. Deriving it this way\n",
    "# rather than hard-coding an angle keeps it correct for any ``qubit_rotation``.\n",
    "etched.ports[\"junction\"].orientation = (\n",
    "    etched.ports[\"junction\"].orientation + 90.0\n",
    ") % 360.0\n",
    "\n",
    "junction_sheet = kdb.Region(\n",
    "    kdb.DBox(\n",
    "        -JUNCTION_PORT_WIDTH / 2,\n",
    "        -JUNCTION_PORT_LENGTH / 2,\n",
    "        JUNCTION_PORT_WIDTH / 2,\n",
    "        JUNCTION_PORT_LENGTH / 2,\n",
    "    ).to_itype(etched.kcl.dbu)\n",
    ")\n",
    "assert (junction_sheet & metal).is_empty(), \"junction sheet overlaps pad metal\"\n",
    "assert len(metal.interacting(junction_sheet.sized(1))) == 2, (\n",
    "    \"junction sheet must touch both pad edges\"\n",
    ")\n",
    "\n",
    "etched.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7255f9b",
   "metadata": {},
   "source": [
    "## Analytical resonator estimate\n",
    "\n",
    "The CPW cross-section model gives a first estimate of the readout frequency\n",
    "before any mesh is built. A quarter-wave resonator of length $L$\n",
    "resonates at $f_\\text{r} = v_\\text{p} / 4L$ with phase velocity\n",
    "$v_\\text{p} = c_0 / \\sqrt{\\varepsilon_{\\text{eff}}}$, where\n",
    "$\\varepsilon_{\\text{eff}}$ comes from the conformal-mapping CPW model\n",
    "in {func}`~qpdk.models.cpw_parameters`\n",
    "{cite:p}`simonsCoplanarWaveguideCircuits2001,m.pozarMicrowaveEngineering2012`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ac9a55f9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-27T13:15:14.041023Z",
     "iopub.status.busy": "2026-10-27T13:15:14.040921Z",
     "iopub.status.idle": "2026-10-27T13:15:14.443948Z",
     "shell.execute_reply": "2026-10-27T13:15:14.443248Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m2026-10-27 15:15:14.371\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36m<module>\u001b[0m:\u001b[36m5\u001b[0m - \u001b[1mCPW width 10.0 µm, gap 6.0 µm\u001b[0m\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m2026-10-27 15:15:14.393\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36m<module>\u001b[0m:\u001b[36m6\u001b[0m - \u001b[1mε_eff = 6.065, Z0 = 49.3 Ω\u001b[0m\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m2026-10-27 15:15:14.441\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36m<module>\u001b[0m:\u001b[36m23\u001b[0m - \u001b[1mRouted length 7020.0 µm, PEC-sheet quarter-wave estimate 4.2793 GHz\u001b[0m\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m2026-10-27 15:15:14.441\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36m<module>\u001b[0m:\u001b[36m27\u001b[0m - \u001b[1mFinite-thickness PDK estimate: 4.3351 GHz\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "RESONATOR_CROSS_SECTION = \"cpw\"\n",
    "\n",
    "width, gap = get_cpw_dimensions(RESONATOR_CROSS_SECTION)\n",
    "epsilon_eff, z0 = cpw_parameters(width, gap)\n",
    "logger.info(f\"CPW width {width} µm, gap {gap} µm\")\n",
    "logger.info(\n",
    "    f\"ε_eff = {float(np.real(epsilon_eff)):.3f}, Z0 = {float(np.real(z0)):.1f} Ω\"\n",
    ")\n",
    "\n",
    "routed_length = component.info[\"routed_length\"]\n",
    "pdk_analytical_freq = resonator_frequency(\n",
    "    length=routed_length,\n",
    "    cross_section=RESONATOR_CROSS_SECTION,\n",
    "    is_quarter_wave=True,\n",
    ")\n",
    "epsilon_eff_sheet = cpw_epsilon_eff(\n",
    "    width * 1e-6,\n",
    "    gap * 1e-6,\n",
    "    500e-6,\n",
    "    material_properties[\"Si\"][\"relative_permittivity\"],\n",
    ")\n",
    "analytical_freq = c_0 / float(epsilon_eff_sheet) ** 0.5 / (4 * routed_length * 1e-6)\n",
    "logger.info(\n",
    "    f\"Routed length {routed_length:.1f} µm, PEC-sheet quarter-wave estimate \"\n",
    "    f\"{analytical_freq / 1e9:.4f} GHz\"\n",
    ")\n",
    "logger.info(f\"Finite-thickness PDK estimate: {pdk_analytical_freq / 1e9:.4f} GHz\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "190534d6",
   "metadata": {},
   "source": [
    "Both estimates use the actual routed CPW length. The PDK model includes\n",
    "the 200 nm metal thickness, while Palace uses a zero-thickness PEC sheet.\n",
    "Matching that assumption lowers the estimate from 4.3351 to 4.2793 GHz.\n",
    "Neither formula includes the open-end plate, feed and qubit loading,\n",
    "or the bends. The plate is a capacitive termination, not another length\n",
    "of uniform CPW."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0550a262",
   "metadata": {},
   "source": [
    "## Junction-sheet geometry\n",
    "\n",
    "The electrical sheet ends at the pad edges. Extending it over the pads\n",
    "removes PEC boundary coverage from the overlap patches in the generated\n",
    "mesh, even though the positive-metal polygons still look intact. Checking\n",
    "those polygons alone does not establish correct electrical contact.\n",
    "\n",
    "With the enlarged layout, a 0.70 µm mesh, order 2 and 10 nH held fixed:\n",
    "\n",
    "| sheet length (µm) | sheet width (µm) | junction-sensitive pole (GHz) | readout (GHz) | reported $\\lvert p_\\text{J}\\rvert$ |\n",
    "| ---: | ---: | ---: | ---: | ---: |\n",
    "| 25 | 33.446 | 2.235890 | 4.189576 | 0.1215 |\n",
    "| 15 | 33.446 | 6.323322 | 4.189379 | 0.9961 |\n",
    "| 15 | 2 | 6.324632 | 4.189368 | 0.9950 |\n",
    "\n",
    "The overlapping sheet's weakly participating branch is not used for tuning.\n",
    "The setup uses an exact-gap sheet and a narrow 2 µm electrical width,\n",
    "independent of the junction's wider layout-placement marker. The readout\n",
    "moves by only 0.20 MHz in this off-resonant control, so this port issue is\n",
    "separate from the much larger CPW-estimate offset."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6921f7f1",
   "metadata": {},
   "source": [
    "## Eigenmode setup with gsim\n",
    "\n",
    "gsim turns the converted layout into a 3-D model: a `LayerStack` assigns each\n",
    "GDS layer a material and a z-extent, and `EigenmodeSim` configures the ports\n",
    "and the eigenmode search. The setup is shown as code without meshing during\n",
    "notebook execution; gsim and Palace are installed separately. The\n",
    "configuration matches the saved solve below.\n",
    "\n",
    "```python\n",
    "import json\n",
    "\n",
    "from gsim.palace import EigenmodeSim\n",
    "\n",
    "from qpdk.simulation import single_chip_stack\n",
    "\n",
    "# 500 µm of microwave silicon and 500 µm of air above it; the substrate\n",
    "# uses the qpdk material properties (eps_r = 11.45, tan d = 2.7e-6), so the\n",
    "# FEM models the same chip as the analytical CPW models of the previous\n",
    "# section.\n",
    "stack = single_chip_stack(substrate_thickness=500.0, vacuum_thickness=500.0)\n",
    "\n",
    "sim = EigenmodeSim()\n",
    "sim.set_geometry(etched)\n",
    "sim.set_stack(stack)\n",
    "sim.set_solver(order=ELEMENT_ORDER, solver_type=\"MUMPS\")\n",
    "\n",
    "# Retune the linear junction inductance for the enlarged pads.\n",
    "# resistance=0 overrides the R = 50 Ω that gsim's default\n",
    "# port impedance would emit: the linearized junction is purely reactive, so\n",
    "# it shifts the qubit-like mode but adds no dissipation. The narrow sheet\n",
    "# joins the pad edges across the 15 µm gap. Its electrical width is separate\n",
    "# from the wider junction placement marker in the layout cell.\n",
    "sim.add_port(\n",
    "    \"junction\",\n",
    "    layer=\"SUPERCONDUCTOR\",\n",
    "    length=JUNCTION_PORT_LENGTH,\n",
    "    inductance=JUNCTION_INDUCTANCE,\n",
    "    resistance=0.0,\n",
    ")\n",
    "\n",
    "# Probeline feeds as 50 Ω CPW lumped ports, one per end.\n",
    "sim.add_cpw_port(\"coupling_o1\", layer=\"SUPERCONDUCTOR\", s_width=10.0, gap_width=6.0, length=5.0)\n",
    "sim.add_cpw_port(\"coupling_o2\", layer=\"SUPERCONDUCTOR\", s_width=10.0, gap_width=6.0, length=5.0)\n",
    "\n",
    "# Search below the pair so mesh shifts do not exclude the qubit mode.\n",
    "sim.set_eigenmode(target=3500000000.0, num_modes=2, save=2)\n",
    "\n",
    "sim.set_output_dir(\"./sim_palace_qubit_resonator\")\n",
    "sim.mesh(preset=\"default\", refined_mesh_size=NEAR_MESH, auto_size=False)\n",
    "sim.write_config()\n",
    "config_path = Path(\"sim_palace_qubit_resonator/config.json\")\n",
    "config = json.loads(config_path.read_text())\n",
    "config[\"Solver\"][\"Linear\"].update({\n",
    "    \"Tol\": 1e-5,\n",
    "    \"MaxIts\": 20,\n",
    "    \"MGMaxLevels\": ELEMENT_ORDER,\n",
    "    \"PCMatReal\": False,\n",
    "    \"PCMatShifted\": False,\n",
    "    \"ComplexCoarseSolve\": True,\n",
    "    \"ColumnOrdering\": \"ParMETIS\",\n",
    "})\n",
    "config[\"Solver\"][\"Eigenmode\"][\"Tol\"] = 1e-5\n",
    "config_path.write_text(json.dumps(config, indent=2))\n",
    "```\n",
    "\n",
    "Four details of this setup:\n",
    "\n",
    "- The substrate carries the qpdk microwave-silicon loss tangent\n",
    "  ($\\tan\\delta = 2.7 \\times 10^{-6}$\n",
    "  {cite:p}`checchinMeasurementLowTemperatureLoss2022`), which contributes\n",
    "  to the mode loss budget below.\n",
    "- The junction port makes the qubit participate in the linear spectrum: the\n",
    "  capacitance between the pads comes from the field solution, the inductive\n",
    "  part from the port. Its isolated linear LC estimate is\n",
    "  $f_\\text{q} \\approx 1 / (2\\pi\\sqrt{L_\\text{J} C_\\Sigma})$\n",
    "  {cite:p}`kochChargeinsensitiveQubitDesign2007a,krantzQuantumEngineersGuide2019`.\n",
    "- Unspecified exterior boundaries use Palace's natural\n",
    "  [PMC condition](https://awslabs.github.io/palace/dev/guide/boundaries/),\n",
    "  which sets the tangential magnetic field to zero. This finite-domain\n",
    "  calculation does not model radiation loss into open space.\n",
    "- `sim.mesh()` and `sim.write_config()` leave a self-contained mesh and\n",
    "  Palace configuration that can be solved with MPI."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae48a45d",
   "metadata": {},
   "source": [
    "## Solving with Palace\n",
    "\n",
    "The saved result used 64 MPI ranks. The mesh and configuration can be\n",
    "solved with any compatible Palace build:\n",
    "\n",
    "```bash\n",
    "cd <simulation directory>  # config.json + palace.msh\n",
    "mpirun -np 64 palace-x86_64.bin config.json\n",
    "```\n",
    "\n",
    "`refined_mesh_size` is the knob that matters for field quality: it sets the\n",
    "element size near the conductors, where the field varies fastest. The\n",
    "comparison below checks sensitivity to mesh size and element order before\n",
    "interpreting the mode spacing.\n",
    "\n",
    "Palace writes one row per mode to `output/palace/eig.csv`: mode index, the\n",
    "real and imaginary parts of the eigenfrequency in GHz, and the quality\n",
    "factor. Two companion tables make mode identification quantitative:\n",
    "`output/palace/port-EPR.csv` for the energy-participation ratio of each\n",
    "lumped port in each mode and `output/palace/port-Q.csv` for the external\n",
    "quality factor per port. The solved rows are embedded below so the analysis\n",
    "runs without the output files."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e176856",
   "metadata": {},
   "source": [
    "## Results: eigenmodes versus the analytical estimate\n",
    "\n",
    "The meander, feed coupling section, and qubit-side route form a single\n",
    "quarter-wave line with a 7020 µm routed length. Among the returned modes,\n",
    "identify the qubit-like mode set by the junction inductance and pad\n",
    "capacitance, and the dressed readout resonance near the analytical estimate.\n",
    "Frequencies and separations below refer to the linearized eigenproblem."
   ]
  },
  {
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   "execution_count": 8,
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     "hide-input"
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m2026-10-27 15:15:14.447\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36m<module>\u001b[0m:\u001b[36m13\u001b[0m - \u001b[1m  m  Re{f} (GHz)      Q_raw         p_J       Q_ext\u001b[0m\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m2026-10-27 15:15:14.447\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36m<module>\u001b[0m:\u001b[36m17\u001b[0m - \u001b[1m  1       4.0074   1.51e+05   -9.87e-01    2.56e+05\u001b[0m\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m2026-10-27 15:15:14.448\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36m<module>\u001b[0m:\u001b[36m17\u001b[0m - \u001b[1m  2       4.2081   2.54e+03   -1.08e-02    2.57e+03\u001b[0m\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m2026-10-27 15:15:14.448\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36m<module>\u001b[0m:\u001b[36m17\u001b[0m - \u001b[1m  3       8.4870   6.05e+02    5.55e-09    5.76e+02\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "# Palace output of the 7020 µm run, embedded so the analysis\n",
    "# runs without the cluster: eig.csv (mode index, Re{f} (GHz), Q),\n",
    "# port-EPR.csv (junction participation p[1]) and port-Q.csv (external quality\n",
    "# factors of the two probeline ports).\n",
    "eig_m = np.arange(1, 4)\n",
    "eig_f_re = np.array([4.007439068267, 4.208077689608, 8.486962393119])\n",
    "eig_q = np.array([150540.2070389, 2541.489411393, 605.1540704776])\n",
    "junction_epr = np.array([-0.9872826787461, -0.01084785422144, 5.547934304728e-09])\n",
    "q_ext_feed1 = np.array([511535.7587765, 5139.324298906, 1165.239709009])\n",
    "q_ext_feed2 = np.array([514440.1447817, 5145.719287397, 1139.911082345])\n",
    "q_ext_feed = 1 / (1 / q_ext_feed1 + 1 / q_ext_feed2)\n",
    "\n",
    "logger.info(f\"{'m':>3} {'Re{f} (GHz)':>12} {'Q_raw':>10} {'p_J':>11} {'Q_ext':>11}\")\n",
    "for m, f_re, q, p1, qe in zip(\n",
    "    eig_m, eig_f_re, eig_q, junction_epr, q_ext_feed, strict=True\n",
    "):\n",
    "    logger.info(f\"{m:>3} {f_re:>12.4f} {q:>10.2e} {p1:>11.2e} {qe:>11.2e}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "680f38f3",
   "metadata": {},
   "source": [
    "Three features identify the modes, and they must agree:\n",
    "\n",
    "1. **Frequency**: the qubit and readout form a nearby pair; the analytical\n",
    "   quarter-wave estimate is only a starting point for the readout.\n",
    "2. **Junction participation**: `port-EPR.csv` reports the fraction\n",
    "   $p_{m,\\text{J}}$ of each mode's energy stored in the junction port\n",
    "   {cite:p}`minevEnergyParticipationQuantization2021`. The qubit-like mode\n",
    "   carries by far the largest participation; a resonator-like mode keeps\n",
    "   most of its inductive energy in the CPW trace and participates much less.\n",
    "3. **Port dissipation**: `port-Q.csv` gives the external quality factor of\n",
    "   each mode through the 50 Ω probeline ports. The readout resonance couples\n",
    "   to the feedline and gets a finite $Q_{\\text{ext}}$ (with both feed\n",
    "   ports adding in parallel, $1/Q = 1/Q_{\\text{diel}} + \\sum 1/Q_{\\text{ext}}$).\n",
    "   The nearby qubit-like mode couples more weakly to the feedline, though its\n",
    "   external loss need not be negligible at this small detuning. Silicon loss\n",
    "   alone would give $Q_{\\text{diel}} \\approx 1 / (p_{\\text{diel}} \\tan\\delta)$\n",
    "   with $\\tan\\delta = 2.7 \\times 10^{-6}$.\n",
    "\n",
    "No single feature suffices: a heavily feed-coupled spurious\n",
    "mode can have the lowest Q of the spectrum without being the readout\n",
    "resonance. Participation-based identification is the standard in\n",
    "quantitative qubit design: the same EPRs that label the modes also turn the\n",
    "eigenmode data into a quantum Hamiltonian with anharmonicities and couplings\n",
    "{cite:p}`minevEnergyParticipationQuantization2021,niggBlackboxSuperconductingCircuit2012`.\n",
    "The stored port values serve as mode-identification diagnostics here. Energy\n",
    "normalization and loss convergence remain separate checks for a Hamiltonian\n",
    "or lifetime extraction."
   ]
  },
  {
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   "id": "f86cd452",
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m2026-10-27 15:15:14.456\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36m<module>\u001b[0m:\u001b[36m19\u001b[0m - \u001b[1mAnalytical estimate:          4.2793 GHz\u001b[0m\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m2026-10-27 15:15:14.457\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36m<module>\u001b[0m:\u001b[36m20\u001b[0m - \u001b[1mPalace readout resonance:     4.2081 GHz (mode 2, p_J = -1.08e-02)\u001b[0m\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m2026-10-27 15:15:14.457\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36m<module>\u001b[0m:\u001b[36m24\u001b[0m - \u001b[1mRelative offset:              -1.66%\u001b[0m\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m2026-10-27 15:15:14.457\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36m<module>\u001b[0m:\u001b[36m29\u001b[0m - \u001b[1mEquivalent electrical length: 7138.8 µm (+118.8 µm over the routed line)\u001b[0m\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m2026-10-27 15:15:14.458\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36m<module>\u001b[0m:\u001b[36m33\u001b[0m - \u001b[1mReadout raw quality factor:   2.541e+03 (Q_ext = 2.571e+03 through both feed ports)\u001b[0m\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m2026-10-27 15:15:14.458\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36m<module>\u001b[0m:\u001b[36m37\u001b[0m - \u001b[1mQubit-like mode:              4.0074 GHz (mode 1, p_J = -9.87e-01)\u001b[0m\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m2026-10-27 15:15:14.458\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36m<module>\u001b[0m:\u001b[36m41\u001b[0m - \u001b[1mQubit-readout separation:    200.6 MHz\u001b[0m\n"
     ]
    },
    {
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      "text/plain": [
       "<Figure size 3000x1140 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "analytical_ghz = analytical_freq / 1e9\n",
    "# A higher harmonic can carry more junction energy than the fundamental.\n",
    "JUNCTION_PARTICIPATION_FLOOR = 0.5\n",
    "_candidates = np.flatnonzero(np.abs(junction_epr) >= JUNCTION_PARTICIPATION_FLOOR)\n",
    "assert _candidates.size, \"no strongly junction-participating qubit mode\"\n",
    "i_qubit = int(_candidates[np.argmin(eig_f_re[_candidates])])\n",
    "_readout_candidates = np.flatnonzero(np.abs(junction_epr) < 0.05)\n",
    "assert _readout_candidates.size, \"no mode with low junction participation\"\n",
    "i_res = int(\n",
    "    _readout_candidates[\n",
    "        np.argmin(np.abs(eig_f_re[_readout_candidates] - analytical_ghz))\n",
    "    ]\n",
    ")\n",
    "resonator_fem_ghz = eig_f_re[i_res]\n",
    "resonator_q = eig_q[i_res]\n",
    "assert abs(junction_epr[i_res]) < 0.05, (\n",
    "    \"identified readout mode stores too much junction energy\"\n",
    ")\n",
    "logger.info(f\"Analytical estimate:          {analytical_ghz:.4f} GHz\")\n",
    "logger.info(\n",
    "    f\"Palace readout resonance:     {resonator_fem_ghz:.4f} GHz \"\n",
    "    f\"(mode {eig_m[i_res]}, p_J = {junction_epr[i_res]:.2e})\"\n",
    ")\n",
    "logger.info(\n",
    "    f\"Relative offset:              \"\n",
    "    f\"{(resonator_fem_ghz - analytical_ghz) / analytical_ghz:+.2%}\"\n",
    ")\n",
    "effective_length_um = routed_length * analytical_ghz / resonator_fem_ghz\n",
    "logger.info(\n",
    "    f\"Equivalent electrical length: {effective_length_um:.1f} µm \"\n",
    "    f\"({effective_length_um - routed_length:+.1f} µm over the routed line)\"\n",
    ")\n",
    "logger.info(\n",
    "    f\"Readout raw quality factor:   {resonator_q:.3e} \"\n",
    "    f\"(Q_ext = {q_ext_feed[i_res]:.3e} through both feed ports)\"\n",
    ")\n",
    "logger.info(\n",
    "    f\"Qubit-like mode:              {eig_f_re[i_qubit]:.4f} GHz \"\n",
    "    f\"(mode {eig_m[i_qubit]}, p_J = {junction_epr[i_qubit]:.2e})\"\n",
    ")\n",
    "logger.info(\n",
    "    f\"Qubit-readout separation:    \"\n",
    "    f\"{abs(resonator_fem_ghz - eig_f_re[i_qubit]) * 1e3:.1f} MHz\"\n",
    ")\n",
    "\n",
    "fig, axes = plt.subplots(1, 2, figsize=(10, 3.8))\n",
    "axes[0].scatter(eig_f_re, eig_m, color=\"0.55\", s=36)\n",
    "axes[0].scatter(eig_f_re[i_qubit], eig_m[i_qubit], color=\"darkorange\", s=80)\n",
    "axes[0].scatter(eig_f_re[i_res], eig_m[i_res], color=\"steelblue\", s=80)\n",
    "axes[0].axvline(\n",
    "    analytical_ghz,\n",
    "    color=\"crimson\",\n",
    "    linestyle=\"--\",\n",
    "    label=rf\"analytical $f_\\text{{r}}$ = {analytical_ghz:.3f} GHz\",\n",
    ")\n",
    "axes[0].set_xlabel(\"frequency (GHz)\")\n",
    "axes[0].set_ylabel(\"Palace mode\")\n",
    "axes[0].set_yticks(eig_m)\n",
    "axes[0].set_xlim(min(analytical_ghz, eig_f_re.min()) - 0.2, eig_f_re.max() + 0.2)\n",
    "axes[0].legend(loc=\"center left\")\n",
    "axes[1].scatter(\n",
    "    [eig_f_re[i_qubit], resonator_fem_ghz],\n",
    "    [1, 0],\n",
    "    color=[\"darkorange\", \"steelblue\"],\n",
    "    s=90,\n",
    ")\n",
    "axes[1].set_yticks([0, 1], [\"readout\", \"qubit\"])\n",
    "axes[1].set_xlim(\n",
    "    min(eig_f_re[i_qubit], resonator_fem_ghz) - 0.12,\n",
    "    max(eig_f_re[i_qubit], resonator_fem_ghz) + 0.12,\n",
    ")\n",
    "axes[1].set_ylim(-0.4, 1.4)\n",
    "axes[1].set_xlabel(\"frequency (GHz)\")\n",
    "axes[1].set_title(\n",
    "    f\"separation: {abs(resonator_fem_ghz - eig_f_re[i_qubit]) * 1e3:.0f} MHz\"\n",
    ")\n",
    "fig.tight_layout()\n",
    "plt.show()\n",
    "plt.close(fig)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2fa823b4",
   "metadata": {},
   "source": [
    "### Mesh and element-order sensitivity\n",
    "\n",
    "Hold the enlarged geometry and 25.15 nH junction fixed while changing the\n",
    "near-conductor mesh size and polynomial order. The rows below are separate\n",
    "solves of the exact-gap sheet model.\n",
    "\n",
    "| mesh size (µm) | order | qubit (GHz) | readout (GHz) | separation (MHz) |\n",
    "| ---: | ---: | ---: | ---: | ---: |\n",
    "| 0.70 | 2 | 3.990952 | 4.191883 | 200.931 |\n",
    "| 0.55 | 2 | 3.993232 | 4.195651 | 202.420 |\n",
    "| 0.70 | 3 | 4.006395 | 4.206422 | 200.027 |\n",
    "| 0.55 | 3 | 4.007439 | 4.208078 | 200.639 |\n",
    "\n",
    "The mode spacing stays within 2 MHz of 201 MHz, close to the original\n",
    "205 MHz target. At 0.55 µm, increasing the order moves the two frequencies\n",
    "by 14.2 and 12.4 MHz. At order 3, refining from 0.70 to 0.55 µm changes\n",
    "them by 1.0 and 1.7 MHz. Absolute frequencies still need tighter convergence\n",
    "before setting a design tolerance. Quality factors remain loss diagnostics\n",
    "at this eigensolver tolerance."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1996984",
   "metadata": {},
   "source": [
    "### Ground and domain sensitivity\n",
    "\n",
    "The enlarged layout has separate domain checks at order 2 and 0.55 µm,\n",
    "with the exact-gap junction sheet and 25.15 nH held fixed:\n",
    "\n",
    "| ground/domain margin (µm) | vacuum height (µm) | qubit (GHz) | readout (GHz) | separation (MHz) |\n",
    "| ---: | ---: | ---: | ---: | ---: |\n",
    "| 500 | 500 | 3.993232 | 4.195651 | 202.420 |\n",
    "| 1000 | 500 | 3.993259 | 4.195621 | 202.362 |\n",
    "| 500 | 1000 | 3.993157 | 4.195817 | 202.660 |\n",
    "\n",
    "Doubling the ground margin changes the readout by 0.03 MHz, and doubling\n",
    "the vacuum height changes it by 0.17 MHz. Both changes move each mode by\n",
    "less than 0.2 MHz, below the observed mesh and element-order shifts.\n",
    "These checks support the 500 µm ground margin and 500 µm vacuum height.\n",
    "\n",
    "To test the original ground-plane hypothesis, hold the 55 × 110 µm pads,\n",
    "original overlapping junction sheet at 10 nH, 7020 µm CPW route and\n",
    "unextended feed fixed. Only the readout branch is used for these diagnostics.\n",
    "Separate order-2, 0.55 µm runs change the lateral ground and simulation-area\n",
    "margin together. Silicon and vacuum remain 500 µm thick.\n",
    "\n",
    "| ground/domain margin (µm) | readout (GHz) |\n",
    "| ---: | ---: |\n",
    "| 100 | 4.201294 |\n",
    "| 500 | 4.199988 |\n",
    "| 1000 | 4.199863 |\n",
    "\n",
    "Increasing the margin to 500 µm lowers the readout by 1.306 MHz, and doubling\n",
    "it again changes the frequency by only 0.125 MHz. The ground/domain extent\n",
    "accounts for little of the roughly 78 MHz difference from the matching\n",
    "PEC-sheet estimate. The inferred extra electrical length is not an omitted\n",
    "route: the qubit-side CPW arm is already counted in the routed length.\n",
    "\n",
    "### Capacitive termination loading\n",
    "\n",
    "At a 500 µm margin, shorten the open-end plate while retaining the same\n",
    "7020 µm routed CPW length and the original qubit and feed geometry:\n",
    "\n",
    "| plate length (µm) | feed coupling gap (µm) | readout (GHz) |\n",
    "| ---: | ---: | ---: |\n",
    "| 120 | 16 | 4.199988 |\n",
    "| 20 | 16 | 4.251054 |\n",
    "| 20 | 200 | 4.249741 |\n",
    "\n",
    "The 51.066 MHz increase is much larger than the ground-margin shift. The\n",
    "long plate and its 2 µm ground clearance add capacitance at the voltage\n",
    "antinode. The uniform quarter-wave formula omits that termination loading.\n",
    "Moving the feed farther away changes the short-plate result by only\n",
    "-1.312 MHz. Roughly 30 MHz remains below the PEC-sheet estimate. The shorter\n",
    "plate still loads the line, and the bent, nonuniform layout is not an\n",
    "unloaded straight CPW. Mesh and element-order errors also remain in this\n",
    "comparison. These controls do not isolate every contribution.\n",
    "\n",
    "The stored port-EPR values are signed. Mode identification uses their\n",
    "magnitudes as diagnostics. Those magnitudes do not establish a normalized\n",
    "energy budget for Hamiltonian extraction.\n",
    "\n",
    "Magnitude alone is not enough either: a higher harmonic can have a larger\n",
    "junction participation. The qubit mode is the lowest one above the stated\n",
    "participation floor.\n",
    "\n",
    "The qubit-like frequency depends on the junction inductance and the pad\n",
    "capacitance the field solution sees. Inverting\n",
    "$f_\\text{q} = 1/(2\\pi\\sqrt{L_\\text{J} C})$ with the configured junction inductance gives the capacitance a pure\n",
    "LC mode would need at this frequency. It is an effective capacitance for the\n",
    "coupled mode, not a separate extraction of the pad shunt capacitance\n",
    "$C_\\Sigma$. A Hamiltonian extraction needs the full energy-participation\n",
    "treatment {cite:p}`minevEnergyParticipationQuantization2021`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "af9641a8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-27T13:15:14.800431Z",
     "iopub.status.busy": "2026-10-27T13:15:14.800326Z",
     "iopub.status.idle": "2026-10-27T13:15:14.803102Z",
     "shell.execute_reply": "2026-10-27T13:15:14.802606Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m2026-10-27 15:15:14.801\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36m<module>\u001b[0m:\u001b[36m2\u001b[0m - \u001b[1mEffective mode capacitance: 63 fF (from f_q and L_J, assuming a pure LC mode)\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "c_mode = 1 / (2 * np.pi * eig_f_re[i_qubit] * 1e9) ** 2 / JUNCTION_INDUCTANCE\n",
    "logger.info(\n",
    "    f\"Effective mode capacitance: {c_mode * 1e15:.0f} fF\"\n",
    "    \" (from f_q and L_J, assuming a pure LC mode)\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65d24795",
   "metadata": {},
   "source": [
    "A single solve provides a first length correction: with\n",
    "$\\delta = 1 - f_{\\text{FEM}} / f_{\\text{est}}$ the corrected model\n",
    "$f \\approx (1 - \\delta)\\, v_\\text{p} / 4L$ inverts to\n",
    "$L^{*} = (1 - \\delta)\\, v_\\text{p} / (4 f_{\\text{target}})$, which is enough to\n",
    "estimate the routed length for a target readout frequency. The offset need\n",
    "not remain constant when the layout changes, so the adjusted geometry needs\n",
    "its own solve."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21bce1d4",
   "metadata": {},
   "source": [
    "## Field visualization\n",
    "\n",
    "Palace saved the first two eigenfields under\n",
    "`output/palace/paraview/eigenmode/Cycle00000{1,2}/data.pvtu`. The images\n",
    "sample their electric-field magnitude 1 µm above the metal plane at model\n",
    "coordinate $z=501$ µm. The qubit view zooms in on the pads; the readout view\n",
    "shows the full meander. [Palace's visualization guide](https://awslabs.github.io/palace/stable/guide/postprocessing/#visualization-field-spaces-and-interface-values)\n",
    "explains that the tangential field is continuous across tetrahedra but the\n",
    "normal component can jump, leaving triangular seams in a planar cut.\n",
    "We clip the low values and smooth the display over 0.4 µm near the\n",
    "pads and 1.0 µm across the meander. Frequencies and participations use the\n",
    "unsmoothed solver data. Gray lies outside the solved domain, and eigenmode\n",
    "amplitudes have arbitrary normalization. The images are saved in the\n",
    "notebook; the cross-section below marks the sampling plane."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "59e93710",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-27T13:15:14.804233Z",
     "iopub.status.busy": "2026-10-27T13:15:14.804143Z",
     "iopub.status.idle": "2026-10-27T13:15:14.971942Z",
     "shell.execute_reply": "2026-10-27T13:15:14.971340Z"
    }
   },
   "outputs": [
    {
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",
      "text/plain": [
       "<Figure size 1950x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(6.5, 2.4))\n",
    "ax.axhspan(496, 500, color=\"steelblue\", alpha=0.12)\n",
    "ax.axhspan(500, 506, color=\"darkorange\", alpha=0.08)\n",
    "pad_outer = QUBIT_PAD_SIZE[0] + QUBIT_PAD_GAP / 2\n",
    "ax.hlines(\n",
    "    [500, 500],\n",
    "    [-pad_outer, QUBIT_PAD_GAP / 2],\n",
    "    [-QUBIT_PAD_GAP / 2, pad_outer],\n",
    "    color=\"0.2\",\n",
    "    linewidth=5,\n",
    ")\n",
    "ax.axhline(501, color=\"crimson\", linestyle=\"--\", label=\"field sampling plane\")\n",
    "ax.text(-pad_outer, 497.2, \"silicon\")\n",
    "ax.text(-pad_outer, 503.5, \"air\")\n",
    "ax.set_xlim(-pad_outer - 20, pad_outer + 20)\n",
    "ax.set_ylim(496, 506)\n",
    "ax.set_xlabel(\"y across the qubit pad gap (µm)\")\n",
    "ax.set_ylabel(\"z (µm)\")\n",
    "ax.grid(False)\n",
    "ax.legend(loc=\"lower right\")\n",
    "fig.tight_layout()\n",
    "plt.show()\n",
    "plt.close(fig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "16746fe4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-27T13:15:14.973133Z",
     "iopub.status.busy": "2026-10-27T13:15:14.973034Z",
     "iopub.status.idle": "2026-10-27T13:16:07.493042Z",
     "shell.execute_reply": "2026-10-27T13:16:07.492159Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1950x1680 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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      "image/png": 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      "text/plain": [
       "<Figure size 1950x1680 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "field_root = Path(\"sim_palace_qubit_resonator/output/palace/paraview/eigenmode\")\n",
    "\n",
    "\n",
    "def _plot_eigenfield(mode: int, extent: tuple[float, float, float, float]) -> None:\n",
    "    pv = importlib.import_module(\"pyvista\")\n",
    "\n",
    "    samples = 700 if extent[1] - extent[0] < 500 else 1000\n",
    "    x = np.linspace(extent[0], extent[1], samples)\n",
    "    y = np.linspace(extent[2], extent[3], samples)\n",
    "    grid_x, grid_y = np.meshgrid(x, y)\n",
    "    points = np.column_stack((\n",
    "        grid_x.ravel(),\n",
    "        grid_y.ravel(),\n",
    "        np.full(grid_x.size, 501.0),\n",
    "    ))\n",
    "    path = field_root / f\"Cycle{mode:06d}\" / \"data.pvtu\"\n",
    "    sampled = pv.PolyData(points).sample(pv.read(path))\n",
    "    valid = np.asarray(sampled.point_data[\"vtkValidPointMask\"], dtype=bool).reshape(\n",
    "        grid_x.shape\n",
    "    )\n",
    "    e_real = np.asarray(sampled.point_data[\"E_real\"], dtype=float)\n",
    "    e_imag = np.asarray(sampled.point_data[\"E_imag\"], dtype=float)\n",
    "    magnitude = np.sqrt(np.sum(e_real**2 + e_imag**2, axis=1)).reshape(grid_x.shape)\n",
    "    positive = magnitude[valid & (magnitude > 0)]\n",
    "    vmax = float(np.percentile(positive, 99.5))\n",
    "    vmin = max(vmax / 150, float(np.percentile(positive, 25)))\n",
    "    sigma_um = 0.4 if x[-1] - x[0] < 500 else 1.0\n",
    "    sigma = (sigma_um / (y[1] - y[0]), sigma_um / (x[1] - x[0]))\n",
    "    log_field = np.log(np.where(valid, np.maximum(magnitude, vmin), vmin))\n",
    "    weight = valid.astype(float)\n",
    "    smooth_weight = gaussian_filter(weight, sigma)\n",
    "    shown = np.where(\n",
    "        valid,\n",
    "        np.exp(\n",
    "            gaussian_filter(log_field * weight, sigma)\n",
    "            / np.maximum(smooth_weight, 1e-12)\n",
    "        ),\n",
    "        np.nan,\n",
    "    )\n",
    "    cmap = plt.get_cmap(\"magma\").copy()\n",
    "    cmap.set_bad(\"0.94\")\n",
    "\n",
    "    fig, ax = plt.subplots(figsize=(6.5, 5.6))\n",
    "    image = ax.imshow(\n",
    "        shown,\n",
    "        origin=\"lower\",\n",
    "        extent=extent,\n",
    "        cmap=cmap,\n",
    "        norm=LogNorm(vmin=vmin, vmax=vmax),\n",
    "        interpolation=\"none\",\n",
    "    )\n",
    "    ax.set_aspect(\"equal\")\n",
    "    ax.grid(False)\n",
    "    ax.set_xlabel(\"x (µm)\")\n",
    "    ax.set_ylabel(\"y (µm)\")\n",
    "    ax.set_title(f\"Mode {mode}: {eig_f_re[mode - 1]:.3f} GHz\")\n",
    "    fig.colorbar(image, ax=ax, label=r\"$|\\mathbf{E}|$ (V/m)\")\n",
    "    plt.show()\n",
    "    plt.close(fig)\n",
    "\n",
    "\n",
    "if field_root.exists():\n",
    "    _plot_eigenfield(int(eig_m[i_qubit]), (-230, 230, -250, 250))\n",
    "    bounds = component.dbbox()\n",
    "    _plot_eigenfield(\n",
    "        int(eig_m[i_res]), (bounds.left, bounds.right, bounds.bottom, bounds.top)\n",
    "    )\n",
    "else:\n",
    "    logger.info(\"Palace field files are needed to regenerate the embedded plots\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed1e1809",
   "metadata": {},
   "source": [
    "\n",
    "The qubit-like mode concentrates field across the pad gap and along the pad\n",
    "edges. Complex $E_z$ probes above the two pad interiors are\n",
    "180 degrees out of phase in this mode, consistent with a differential\n",
    "pad excitation. The readout mode follows the meander as a quarter-wave standing wave.\n",
    "Their small detuning leaves some field on both parts of the layout, consistent\n",
    "with the nonzero junction and feed-port participation of each mode."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7aa21fa1",
   "metadata": {},
   "source": [
    "## Summary\n",
    "\n",
    "A qpdk layout goes to a full-wave eigenmode spectrum with no commercial\n",
    "solver license. With larger qubit pads, a longer probeline and a larger\n",
    "domain, retuning the junction inductance retains a nearby qubit/readout\n",
    "pair. Junction participation, feed-port coupling and the saved\n",
    "electric fields distinguish the two. The CPW estimate gives a starting\n",
    "length; the Palace solve checks the adjusted layout.\n",
    "\n",
    "The SAX circuit models in {doc}`all_models` and {doc}`circuit_simulation_demo`\n",
    "miss the geometry-dependent effects captured here (fringing, loading, meander\n",
    "parasitics) but cost almost nothing, so the two complement each other:\n",
    "circuit models to explore, FEM to verify and calibrate. The next step,\n",
    "extracting the Hamiltonian, anharmonicities and dispersive couplings from\n",
    "these eigenmodes via energy participation\n",
    "{cite:p}`minevEnergyParticipationQuantization2021,niggBlackboxSuperconductingCircuit2012`,\n",
    "is what tools such as [pyEPR](https://github.com/zlatko-minev/pyEPR) do."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b19700f7",
   "metadata": {},
   "source": [
    "## References\n",
    "\n",
    "```{bibliography}\n",
    ":filter: docname in docnames\n",
    "```"
   ]
  }
 ],
 "metadata": {
  "jupytext": {
   "main_language": "python"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.14"
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  "qpdk_palace_run": {
   "configuration": {
    "Boundaries": {
     "Conductivity": [],
     "LumpedPort": [
      {
       "Attributes": [
        4
       ],
       "Direction": "-Y",
       "Excitation": false,
       "Index": 1,
       "L": 2.515e-08,
       "R": 0
      },
      {
       "Elements": [
        {
         "Attributes": [
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         ],
         "Direction": "+Y"
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        {
         "Attributes": [
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         "Direction": "-Y"
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       ],
       "Excitation": false,
       "Index": 2,
       "R": 50
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      {
       "Elements": [
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         "Attributes": [
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         "Direction": "-Y"
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         "Attributes": [
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         "Direction": "+Y"
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       "Excitation": false,
       "Index": 3,
       "R": 50
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     ],
     "PEC": {
      "Attributes": [
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      ]
     },
     "WavePort": []
    },
    "Domains": {
     "Materials": [
      {
       "Attributes": [
        1
       ],
       "LossTan": 2.7e-06,
       "Permittivity": 11.45
      },
      {
       "Attributes": [
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       ],
       "LossTan": 0.0,
       "Permittivity": 1.0
      }
     ],
     "Postprocessing": {
      "Energy": [],
      "Probe": []
     }
    },
    "Model": {
     "L0": 1e-06,
     "Mesh": "palace.msh",
     "Refinement": {
      "MaxIts": 0,
      "Tol": 0.01,
      "UniformLevels": 0
     }
    },
    "Problem": {
     "Output": "output-final-fields",
     "Type": "Eigenmode",
     "Verbose": 3
    },
    "Solver": {
     "Device": "CPU",
     "Eigenmode": {
      "N": 2,
      "Save": 2,
      "Target": 3.5,
      "Tol": 1e-05
     },
     "Linear": {
      "ColumnOrdering": "ParMETIS",
      "ComplexCoarseSolve": true,
      "DivFreeMaxIts": 0,
      "DivFreeTol": 1e-06,
      "EstimatorMaxIts": 0,
      "EstimatorTol": 1e-06,
      "KSPType": "GMRES",
      "MGMaxLevels": 3,
      "MaxIts": 20,
      "PCMatReal": false,
      "PCMatShifted": false,
      "Tol": 1e-05,
      "Type": "MUMPS"
     },
     "Order": 3
    }
   },
   "configuration_sha256": "3047d954611f1f466c7feb7db11a66d47f132274939aa662862a5b424054246e",
   "field_probes": [
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     "differential_fraction": 1.0,
     "lower_ez": [
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     "mode": 1,
     "phase_difference_degrees": -179.9998779296875,
     "port_tangential_integral_x": 0.13701136222077115,
     "port_tangential_integral_y": 27.899541619593432,
     "port_x_fraction": 0.004886883288029132,
     "sheet_inductive_energy": 6.5936259030757105e-12,
     "upper_ez": [
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    },
    {
     "differential_fraction": 0.9999999403953552,
     "lower_ez": [
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     "mode": 2,
     "phase_difference_degrees": -179.98741149902344,
     "port_tangential_integral_x": 0.0016664324218253522,
     "port_tangential_integral_y": 0.3380125686869918,
     "port_x_fraction": 0.004905903563027453,
     "sheet_inductive_energy": 7.24493916292113e-14,
     "upper_ez": [
      7876.35595703125,
      -60.95281982421875
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    }
   ],
   "mesh_sha256": "a00996d6f46086b498bc3ed3878d0c88497b8a483bbd712edeaf8cd9ba2b9fc8",
   "mpi_ranks": 64,
   "parameters": {
    "lj_nh": 25.15,
    "mesh_um": 0.55,
    "name": "final-fields",
    "order": 3,
    "port_length": 15.0,
    "port_width": 2.0,
    "render": true,
    "save": 2,
    "target_ghz": 3.5
   },
   "slurm_job_id": "21229113",
   "solver_sha256": "1eba2d93ad2bfe4c9e328ee067c7a72fb385afe329d00514a7abe140a852bebe",
   "template_sha256": "a02280cf2100423f100449113e1893969abded67c356dd76ce5ee1ab4d034f28"
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