{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "0",
   "metadata": {},
   "source": [
    "# HFSS Eigenmode Simulation of a CPW Resonator\n",
    "\n",
    "This notebook demonstrates how to set up and run an eigenmode simulation\n",
    "of a superconducting coplanar waveguide (CPW) resonator using PyAEDT\n",
    "(Ansys HFSS Python interface).\n",
    "\n",
    "Eigenmode analysis finds the natural resonant frequencies and Q-factors\n",
    "of undriven electromagnetic structures - essential for designing\n",
    "superconducting qubits and resonators.\n",
    "\n",
    "**Prerequisites:**\n",
    "- Ansys HFSS installed (requires license)\n",
    "- Install hfss extras: `uv sync --extra hfss` or `pip install qpdk[hfss]`\n",
    "\n",
    "**References:**\n",
    "- PyAEDT Documentation: https://aedt.docs.pyansys.com/\n",
    "- HFSS Eigenmode Examples: https://examples.aedt.docs.pyansys.com/"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1",
   "metadata": {},
   "source": [
    "## Setup and Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-04-29T08:37:46.060417Z",
     "iopub.status.busy": "2026-04-29T08:37:46.060351Z",
     "iopub.status.idle": "2026-04-29T08:37:46.062437Z",
     "shell.execute_reply": "2026-04-29T08:37:46.062234Z"
    },
    "tags": [
     "hide-input",
     "hide-output"
    ]
   },
   "outputs": [],
   "source": [
    "import sys\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",
    "    ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-04-29T08:37:46.063074Z",
     "iopub.status.busy": "2026-04-29T08:37:46.062973Z",
     "iopub.status.idle": "2026-04-29T08:37:46.358481Z",
     "shell.execute_reply": "2026-04-29T08:37:46.358147Z"
    },
    "tags": [
     "hide-input",
     "hide-output"
    ]
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import tempfile\n",
    "import time\n",
    "from pathlib import Path\n",
    "\n",
    "from ansys.aedt.core import Hfss, settings\n",
    "from IPython.display import Image, display\n",
    "from scipy.optimize import minimize_scalar"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4",
   "metadata": {
    "lines_to_next_cell": 0
   },
   "source": [
    "## Create a CPW Resonator Component\n",
    "\n",
    "First, let's create a simple quarter-wave resonator using QPDK's component library.\n",
    "We will use the `resonator_frequency` helper to find the length needed for a 5 GHz resonance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-04-29T08:37:46.359721Z",
     "iopub.status.busy": "2026-04-29T08:37:46.359580Z",
     "iopub.status.idle": "2026-04-29T08:37:48.837526Z",
     "shell.execute_reply": "2026-04-29T08:37:48.837249Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Resonator bounding box: <bound method UMGeometricObject.bbox of Component(name=resonator_L6086p511634681758_M4_BSbend_circular_CScopla_89320bc0, ports=['o1', 'o2'], pins=[], instances=['straight_L966p0263269363516_CScoplanar_waveguide_WNone_N2_0_0', 'bend_circular_A180_R100_NNone_CScoplanar_waveguide_WNon_f5b64f4d_966026_0_M', 'straight_L966p0263269363516_CScoplanar_waveguide_WNone_N2_966026_-200000_A180', 'bend_circular_A180_R100_NNone_CScoplanar_waveguide_WNon_f5b64f4d_0_-200000_A180', 'straight_L966p0263269363516_CScoplanar_waveguide_WNone_N2_0_-400000', 'bend_circular_A180_R100_NNone_CScoplanar_waveguide_WNon_f5b64f4d_966026_-400000_M', 'straight_L966p0263269363516_CScoplanar_waveguide_WNone_N2_966026_-600000_A180', 'bend_circular_A180_R100_NNone_CScoplanar_waveguide_WNon_f5b64f4d_0_-600000_A180', 'straight_L966p0263269363516_CScoplanar_waveguide_WNone_N2_0_-800000', 'rectangle_gdsfactorypcomponentspshapesprectangle_S6_22__2d316d18_-3000_0'], locked=True, kcl=DEFAULT)>\n",
      "Expected quarter-wave frequency: ~5.00 GHz\n",
      "Calculated length: 6086.5 µm\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from qpdk import PDK\n",
    "from qpdk.cells.resonator import resonator\n",
    "from qpdk.config import PATH\n",
    "from qpdk.models.resonator import resonator_frequency\n",
    "from qpdk.simulation import HFSS, prepare_component_for_aedt\n",
    "from qpdk.tech import coplanar_waveguide\n",
    "\n",
    "PDK.activate()\n",
    "\n",
    "# Create a meandering quarter-wave resonator\n",
    "# With default CPW dimensions (10µm width, 6µm gap)\n",
    "cpw_cross_section = coplanar_waveguide(width=10, gap=6)\n",
    "\n",
    "# Calculate length for 5 GHz target\n",
    "target_f_hz = 5e9\n",
    "\n",
    "\n",
    "# Use scipy.optimize to find the exact length for the target frequency\n",
    "def objective(length):\n",
    "    \"\"\"Objective function to find the exact length for the target frequency.\n",
    "\n",
    "    Returns:\n",
    "        Absolute difference between calculated and target frequency.\n",
    "    \"\"\"\n",
    "    f_res = resonator_frequency(\n",
    "        length=length, cross_section=cpw_cross_section, is_quarter_wave=True\n",
    "    )\n",
    "    return abs(f_res - target_f_hz)\n",
    "\n",
    "\n",
    "result = minimize_scalar(objective, bounds=(1000, 10000), method=\"bounded\")\n",
    "current_length = result.x\n",
    "\n",
    "res_component = resonator(\n",
    "    length=current_length,\n",
    "    meanders=4,  # Number of meander turns\n",
    "    cross_section=cpw_cross_section,\n",
    "    open_start=True,  # Open end (voltage antinode)\n",
    "    open_end=False,  # Shorted end (voltage node)\n",
    ")\n",
    "\n",
    "# Visualize the component\n",
    "res_component.plot()\n",
    "print(f\"Resonator bounding box: {res_component.bbox}\")\n",
    "print(f\"Expected quarter-wave frequency: ~{target_f_hz / 1e9:.2f} GHz\")\n",
    "print(f\"Calculated length: {current_length:.1f} µm\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6",
   "metadata": {},
   "source": [
    "## Initialize HFSS Project\n",
    "\n",
    "Now we'll set up an HFSS project for eigenmode analysis.\n",
    "The simulation will find the natural resonant modes of the structure.\n",
    "\n",
    "**Note:** This section requires Ansys HFSS to be installed and licensed.\n",
    "The code is wrapped in a try-except block for demonstration purposes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-04-29T08:37:48.838712Z",
     "iopub.status.busy": "2026-04-29T08:37:48.838412Z",
     "iopub.status.idle": "2026-04-29T08:37:48.840293Z",
     "shell.execute_reply": "2026-04-29T08:37:48.840051Z"
    }
   },
   "outputs": [],
   "source": [
    "# Configuration for HFSS simulation\n",
    "EIGENMODE_CONFIG = {\n",
    "    \"min_frequency_ghz\": 3.0,  # Start searching from 3 GHz\n",
    "    \"num_modes\": 1,  # Find 1 eigenmode\n",
    "    \"max_passes\": 15,  # Maximum adaptive mesh passes\n",
    "    \"min_passes\": 2,\n",
    "    \"percent_refinement\": 30,\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8",
   "metadata": {
    "lines_to_next_cell": 0
   },
   "source": [
    "## Build HFSS Model (Example Code)\n",
    "\n",
    "The following code demonstrates how to:\n",
    "1. Create an HFSS project with eigenmode solution type\n",
    "2. Build the CPW geometry in HFSS\n",
    "3. Add substrate and boundary conditions\n",
    "4. Configure eigenmode analysis\n",
    "5. Run the simulation and extract results\n",
    "\n",
    "```{note}\n",
    "This code requires Ansys HFSS to be installed. The example below shows\n",
    "the structure of a complete simulation workflow.\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-04-29T08:37:48.841020Z",
     "iopub.status.busy": "2026-04-29T08:37:48.840947Z",
     "iopub.status.idle": "2026-04-29T08:39:15.461971Z",
     "shell.execute_reply": "2026-04-29T08:39:15.461661Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PyAEDT INFO: Python version 3.12.13 (main, Mar  3 2026, 14:59:34) [Clang 21.1.4 ].\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PyAEDT INFO: PyAEDT version 0.26.2.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PyAEDT INFO: Initializing new Desktop session.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PyAEDT INFO: AEDT version 2025.2.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PyAEDT INFO: New AEDT session is starting on gRPC port 40607.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PyAEDT INFO: Starting new AEDT gRPC session on port 40607.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PyAEDT INFO: Launching AEDT server with gRPC transport mode: TransportMode.UDS\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PyAEDT INFO: Electronics Desktop started on gRPC port 40607 after 9.2 seconds.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PyAEDT INFO: AEDT installation Path /usr/ansys_inc/v252/AnsysEM\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PyAEDT INFO: Connected to AEDT gRPC session on port 40607.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PyAEDT WARNING: Service Pack is not detected. PyAEDT is currently connecting in Insecure Mode.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PyAEDT WARNING: Please download and install latest Service Pack to use connect to AEDT in Secure Mode.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PyAEDT INFO: Project resonator_eigenmode has been created.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PyAEDT INFO: Added design 'CPW_Resonator' of type HFSS.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PyAEDT INFO: AEDT objects correctly read\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PyAEDT INFO: Modeler class has been initialized! Elapsed time: 0m 0sec\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "HFSS project created: /tmp/tmp2vcqocpe.ansys_qpdk/resonator_eigenmode.aedt\n",
      "Design name: CPW_Resonator\n",
      "Solution type: Eigenmode\n"
     ]
    }
   ],
   "source": [
    "# Example HFSS eigenmode simulation workflow\n",
    "# This code block demonstrates the full workflow but requires HFSS license\n",
    "\n",
    "# Ensure Ansys path is set so PyAEDT can find it\n",
    "ansys_default_path = \"/usr/ansys_inc/v252/AnsysEM\"\n",
    "if \"ANSYSEM_ROOT252\" not in os.environ and Path(ansys_default_path).exists():\n",
    "    os.environ[\"ANSYSEM_ROOT252\"] = ansys_default_path\n",
    "\n",
    "settings.use_grpc_uds = False\n",
    "\n",
    "\n",
    "# Create temporary directory for project\n",
    "temp_dir = tempfile.TemporaryDirectory(suffix=\".ansys_qpdk\")\n",
    "project_path = Path(temp_dir.name) / \"resonator_eigenmode.aedt\"\n",
    "\n",
    "# Initialize HFSS with Eigenmode solution\n",
    "hfss = Hfss(\n",
    "    project=str(project_path),\n",
    "    design=\"CPW_Resonator\",\n",
    "    solution_type=\"Eigenmode\",\n",
    "    non_graphical=False,\n",
    "    new_desktop=True,\n",
    "    version=\"2025.2\",\n",
    ")\n",
    "hfss.modeler.model_units = \"um\"\n",
    "\n",
    "print(f\"HFSS project created: {hfss.project_file}\")\n",
    "print(f\"Design name: {hfss.design_name}\")\n",
    "print(f\"Solution type: {hfss.solution_type}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10",
   "metadata": {},
   "source": [
    "## Build CPW Geometry in HFSS\n",
    "\n",
    "Import the gdsfactory component geometry into HFSS using native GDS import.\n",
    "This uses `Hfss.import_gds_3d` which automatically handles 3D layer mapping\n",
    "based on the QPDK LayerStack."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "11",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-04-29T08:39:15.462848Z",
     "iopub.status.busy": "2026-04-29T08:39:15.462775Z",
     "iopub.status.idle": "2026-04-29T08:39:16.752639Z",
     "shell.execute_reply": "2026-04-29T08:39:16.752249Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PyAEDT INFO: GDS layer imported with elevations and thickness.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PyAEDT INFO: Boundary Perfect E PEC_Sheets has been created.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GDS import successful: True\n",
      "PyAEDT INFO: Materials class has been initialized! Elapsed time: 0m 0sec\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Created substrate: Substrate\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PyAEDT INFO: Boundary Perfect E PEC_Boundary has been created.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Created air region with PEC boundary: AirRegion\n"
     ]
    }
   ],
   "source": [
    "# Prepare component for export\n",
    "res_component = prepare_component_for_aedt(res_component, margin_draw=200)\n",
    "\n",
    "# Initialize HFSS wrapper\n",
    "hfss_sim = HFSS(hfss)\n",
    "\n",
    "# Import the component geometry using native GDS import\n",
    "success = hfss_sim.import_component(res_component, import_as_sheets=True)\n",
    "print(f\"GDS import successful: {success}\")\n",
    "\n",
    "# Add substrate below the component\n",
    "substrate_name = hfss_sim.add_substrate(\n",
    "    res_component,\n",
    "    thickness=500.0,\n",
    "    material=\"silicon\",\n",
    ")\n",
    "print(f\"Created substrate: {substrate_name}\")\n",
    "\n",
    "# Add air region with PEC boundary for eigenmode analysis\n",
    "air_region_name = hfss_sim.add_air_region(\n",
    "    res_component,\n",
    "    height=500.0,\n",
    "    substrate_thickness=500.0,\n",
    "    pec_boundary=True,\n",
    ")\n",
    "print(f\"Created air region with PEC boundary: {air_region_name}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7734f12a",
   "metadata": {},
   "source": [
    "### Geometry Verification\n",
    "Here is the 3D geometry of the CPW resonator in HFSS for eigenmode analysis.\n",
    "\n",
    "![HFSS geometry](../docs/_static/images/hfss_eigenmode_resonator_geom.jpg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b918a62e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-04-29T08:39:16.753559Z",
     "iopub.status.busy": "2026-04-29T08:39:16.753464Z",
     "iopub.status.idle": "2026-04-29T08:39:16.863610Z",
     "shell.execute_reply": "2026-04-29T08:39:16.862814Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PyAEDT INFO: Parsing /tmp/tmp2vcqocpe.ansys_qpdk/resonator_eigenmode.aedt.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PyAEDT INFO: File /tmp/tmp2vcqocpe.ansys_qpdk/resonator_eigenmode.aedt correctly loaded. Elapsed time: 0m 0sec\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PyAEDT INFO: aedt file load time 0.0023860931396484375\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PyAEDT INFO: PostProcessor class has been initialized! Elapsed time: 0m 0sec\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PyAEDT INFO: PostProcessor class has been initialized! Elapsed time: 0m 0sec\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PyAEDT INFO: Post class has been initialized! Elapsed time: 0m 0sec\n"
     ]
    },
    {
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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Ensure HFSS model fits the screen\n",
    "hfss.modeler.fit_all()\n",
    "\n",
    "# Save screenshot\n",
    "img_dir = PATH.repo / \"docs\" / \"_static\" / \"images\"\n",
    "img_dir.mkdir(parents=True, exist_ok=True)\n",
    "hfss_img_path = img_dir / \"hfss_eigenmode_resonator_geom.jpg\"\n",
    "hfss.post.export_model_picture(\n",
    "    full_name=str(hfss_img_path), show_axis=True, show_grid=False, show_ruler=True\n",
    ")\n",
    "\n",
    "# Display in notebook\n",
    "display(Image(filename=str(hfss_img_path)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12",
   "metadata": {},
   "source": [
    "## Configure Eigenmode Analysis\n",
    "\n",
    "Set up the eigenmode solver to find resonant frequencies starting from 3 GHz."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "13",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-04-29T08:39:16.864583Z",
     "iopub.status.busy": "2026-04-29T08:39:16.864503Z",
     "iopub.status.idle": "2026-04-29T08:39:17.083627Z",
     "shell.execute_reply": "2026-04-29T08:39:17.081669Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Eigenmode setup configured:\n",
      "  - Min frequency: 3.0 GHz\n",
      "  - Number of modes: 1\n",
      "  - Max passes: 15\n"
     ]
    }
   ],
   "source": [
    "# Create eigenmode setup\n",
    "setup = hfss.create_setup(name=\"EigenmodeSetup\")\n",
    "\n",
    "setup.props[\"MinimumFrequency\"] = f\"{EIGENMODE_CONFIG['min_frequency_ghz']}GHz\"\n",
    "setup.props[\"NumModes\"] = EIGENMODE_CONFIG[\"num_modes\"]\n",
    "setup.props[\"MaximumPasses\"] = EIGENMODE_CONFIG[\"max_passes\"]\n",
    "setup.props[\"MinimumPasses\"] = EIGENMODE_CONFIG[\"min_passes\"]\n",
    "setup.props[\"PercentRefinement\"] = EIGENMODE_CONFIG[\"percent_refinement\"]\n",
    "setup.props[\"ConvergeOnRealFreq\"] = True\n",
    "setup.props[\"MaxDeltaFreq\"] = 0.1  # 0.1% convergence criterion\n",
    "\n",
    "setup.update()\n",
    "print(\"Eigenmode setup configured:\")\n",
    "print(f\"  - Min frequency: {EIGENMODE_CONFIG['min_frequency_ghz']} GHz\")\n",
    "print(f\"  - Number of modes: {EIGENMODE_CONFIG['num_modes']}\")\n",
    "print(f\"  - Max passes: {EIGENMODE_CONFIG['max_passes']}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14",
   "metadata": {},
   "source": [
    "## Run Simulation\n",
    "\n",
    "Execute the eigenmode analysis. This may take several minutes depending\n",
    "on the mesh complexity and number of modes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "15",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-04-29T08:39:17.084696Z",
     "iopub.status.busy": "2026-04-29T08:39:17.084569Z",
     "iopub.status.idle": "2026-04-29T08:40:54.640420Z",
     "shell.execute_reply": "2026-04-29T08:40:54.640061Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting eigenmode analysis...\n",
      "(This may take several minutes)\n",
      "PyAEDT INFO: Project resonator_eigenmode Saved correctly\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PyAEDT INFO: Key Desktop/ActiveDSOConfigurations/HFSS correctly changed.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PyAEDT INFO: Solving design setup EigenmodeSetup\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PyAEDT INFO: Design setup EigenmodeSetup solved correctly in 0.0h 1.0m 37.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PyAEDT INFO: Key Desktop/ActiveDSOConfigurations/HFSS correctly changed.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Analysis completed in 97.5 seconds\n"
     ]
    }
   ],
   "source": [
    "print(\"Starting eigenmode analysis...\")\n",
    "print(\"(This may take several minutes)\")\n",
    "\n",
    "# Save project before analysis\n",
    "hfss.save_project()\n",
    "\n",
    "# Run the analysis\n",
    "start_time = time.time()\n",
    "success = hfss.analyze_setup(\"EigenmodeSetup\", cores=4)\n",
    "elapsed = time.time() - start_time\n",
    "\n",
    "if not success:\n",
    "    print(\"\\nERROR: HFSS simulation failed!\")\n",
    "    # Try to get more info from HFSS logs if possible\n",
    "else:\n",
    "    print(f\"Analysis completed in {elapsed:.1f} seconds\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16",
   "metadata": {},
   "source": [
    "## Extract Results\n",
    "\n",
    "Get the eigenmode frequencies and Q-factors from the simulation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "17",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-04-29T08:40:54.641371Z",
     "iopub.status.busy": "2026-04-29T08:40:54.641287Z",
     "iopub.status.idle": "2026-04-29T08:40:54.727148Z",
     "shell.execute_reply": "2026-04-29T08:40:54.726838Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PyAEDT INFO: Solution Correctly loaded. Elapsed time: 0m 0sec\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PyAEDT INFO: Solution Correctly parsed. Elapsed time: 0m 0sec\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PyAEDT INFO: Solution Correctly loaded. Elapsed time: 0m 0sec\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PyAEDT INFO: Solution Correctly parsed. Elapsed time: 0m 0sec\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "=== Eigenmode Results ===\n",
      "----------------------------------------\n",
      "Mode 1: f = 4.8350 GHz, Q = 0.0\n",
      "----------------------------------------\n",
      "\n",
      "Comparison with analytical estimate:\n",
      "  Expected (target): 5.0000 GHz\n",
      "  Simulated:         4.8350 GHz\n",
      "  Difference:        3.3%\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "~/dev/quantum-rf-pdk/.venv/lib/python3.12/site-packages/ansys/aedt/core/visualization/post/solution_data.py:648: UserWarning: Method `data_real` is deprecated. Use :func:`get_expression_data` property instead.\n",
      "  warnings.warn(\"Method `data_real` is deprecated. Use :func:`get_expression_data` property instead.\")\n"
     ]
    }
   ],
   "source": [
    "# Extract results using the wrapper\n",
    "sim_results = hfss_sim.get_eigenmode_results(\"EigenmodeSetup\")\n",
    "\n",
    "print(\"\\n=== Eigenmode Results ===\")\n",
    "print(\"-\" * 40)\n",
    "\n",
    "results = {\n",
    "    \"frequencies_ghz\": sim_results[\"frequencies\"],\n",
    "    \"q_factors\": sim_results[\"q_factors\"],\n",
    "}\n",
    "\n",
    "if not results[\"frequencies_ghz\"]:\n",
    "    print(\"No eigenmodes found. Check simulation logs and geometry.\")\n",
    "else:\n",
    "    for i, (freq_ghz, q_factor) in enumerate(\n",
    "        zip(results[\"frequencies_ghz\"], results[\"q_factors\"]), 1\n",
    "    ):\n",
    "        print(f\"Mode {i}: f = {freq_ghz:.4f} GHz, Q = {q_factor:.1f}\")\n",
    "\n",
    "print(\"-\" * 40)\n",
    "\n",
    "# Compare with analytical estimate\n",
    "expected_freq = target_f_hz / 1e9  # Target frequency\n",
    "if results[\"frequencies_ghz\"]:\n",
    "    actual_freq = results[\"frequencies_ghz\"][0]\n",
    "    error_percent = abs(actual_freq - expected_freq) / expected_freq * 100\n",
    "    print(\"\\nComparison with analytical estimate:\")\n",
    "    print(f\"  Expected (target): {expected_freq:.4f} GHz\")\n",
    "    print(f\"  Simulated:         {actual_freq:.4f} GHz\")\n",
    "    print(f\"  Difference:        {error_percent:.1f}%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec402a7c",
   "metadata": {},
   "source": [
    "## Plot Field Solution\n",
    "\n",
    "We can visualize the electric field magnitude of the first eigenmode on the surface of the substrate.\n",
    "\n",
    "![HFSS field](../docs/_static/images/hfss_eigenmode_resonator_field.jpg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "1a2dbca0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-04-29T08:40:54.728061Z",
     "iopub.status.busy": "2026-04-29T08:40:54.727987Z",
     "iopub.status.idle": "2026-04-29T08:40:55.605940Z",
     "shell.execute_reply": "2026-04-29T08:40:55.605588Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PyAEDT INFO: Active Design set to CPW_Resonator\n"
     ]
    },
    {
     "data": {
      "image/jpeg": 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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Ensure the model fits the screen\n",
    "hfss.modeler.fit_all()\n",
    "\n",
    "# Create a surface field plot of the electric field magnitude on the substrate\n",
    "plot = hfss.post.create_fieldplot_surface(\n",
    "    assignment=[substrate_name], quantity=\"Mag_E\", setup=f\"{setup.name} : LastAdaptive\"\n",
    ")\n",
    "\n",
    "# Export the field plot picture\n",
    "hfss_field_img_path = img_dir / \"hfss_eigenmode_resonator_field.jpg\"\n",
    "if plot:\n",
    "    hfss.post.export_model_picture(\n",
    "        full_name=str(hfss_field_img_path),\n",
    "        show_axis=True,\n",
    "        show_grid=False,\n",
    "        show_ruler=True,\n",
    "        field_selections=\"all\",\n",
    "    )\n",
    "else:\n",
    "    print(\"Failed to create field plot\")\n",
    "\n",
    "# Display in notebook\n",
    "display(Image(filename=str(hfss_field_img_path)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18",
   "metadata": {},
   "source": [
    "## Cleanup\n",
    "\n",
    "Close HFSS and clean up temporary files."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "19",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-04-29T08:40:55.607005Z",
     "iopub.status.busy": "2026-04-29T08:40:55.606924Z",
     "iopub.status.idle": "2026-04-29T08:40:57.750487Z",
     "shell.execute_reply": "2026-04-29T08:40:57.750219Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PyAEDT INFO: Project resonator_eigenmode Saved correctly\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "HFSS session closed and temporary files cleaned up\n"
     ]
    }
   ],
   "source": [
    "# Save and close\n",
    "hfss.save_project()\n",
    "# hfss.release_desktop()\n",
    "time.sleep(2)  # Allow HFSS to shut down\n",
    "\n",
    "# Clean up temp directory\n",
    "temp_dir.cleanup()\n",
    "print(\"HFSS session closed and temporary files cleaned up\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20",
   "metadata": {},
   "source": [
    "## Summary\n",
    "\n",
    "This notebook demonstrated:\n",
    "\n",
    "1. **Component Creation**: Using QPDK's `resonator` cell to create a\n",
    "   meandering CPW quarter-wave resonator\n",
    "\n",
    "2. **HFSS Setup**: Initializing PyAEDT with eigenmode solution type\n",
    "\n",
    "3. **Geometry Building**: Converting gdsfactory polygons to HFSS 3D geometry\n",
    "   with proper material assignments (PEC for superconducting metal)\n",
    "\n",
    "4. **Eigenmode Analysis**: Configuring and running the solver to find\n",
    "   resonant frequencies and Q-factors\n",
    "\n",
    "5. **Results Extraction**: Getting mode frequencies and Q-factors for\n",
    "   comparison with analytical models\n",
    "\n",
    "**Key Points for Superconducting Resonators:**\n",
    "- Use PerfectE (PEC) boundaries for superconducting metals at cryogenic temps\n",
    "- Silicon substrate with εᵣ ≈ 11.45 significantly affects resonance frequency\n",
    "- Q-factors from eigenmode analysis represent unloaded Q (internal losses only)\n",
    "- Coupling to external circuits reduces measured Q (loaded Q)\n",
    "\n",
    "**Next Steps:**\n",
    "- Compare eigenmode results with SAX circuit simulations\n",
    "- Add lossy materials to estimate realistic Q-factors\n",
    "- Study coupling effects with driven modal simulations"
   ]
  }
 ],
 "metadata": {
  "jupytext": {
   "main_language": "python"
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.13"
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 },
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