{ "cells": [ { "cell_type": "markdown", "id": "01-title", "metadata": {}, "source": [ "# Fabrication-aware modeling of a diamond nanobeam photonic crystal cavity\n", "\n", "**Author: Alessandro Buzzi, MIT**\n", "\n", "[![Tidy3D](https://img.shields.io/badge/Tidy3D-2.x-blue)](https://docs.flexcompute.com/projects/tidy3d/en/latest/) [![Python 3.10+](https://img.shields.io/badge/Python-3.10%2B-green)](https://python.org) [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)\n", "\n", "---\n", "\n", "This notebook demonstrates a **fabrication-aware Tidy3D workflow** for simulating and characterizing a diamond photonic crystal nanobeam cavity near the **tin-vacancy (SnV)** spectral range. Using cavity and hole specifications embedded directly in the notebook, it combines a broadband resonance search with a narrowband characterization simulation to extract the resonance wavelength, Q factor, mode volume, Purcell factor, collection efficiency, and far-field / polarization response.\n", "\n", "The notebook is self-contained and can be read independently of the associated fabrication study in [arXiv:2601.20025](https://arxiv.org/abs/2601.20025), where related SEM-derived cavity parameters are used in a broader statistical workflow.\n", "\n", "### Practical Note\n", "\n", "- Inputs: embedded cavity and hole geometry specifications, plus the measured thickness and sidewall angle\n", "- Outputs: resonance wavelength, Q factor, near field, mode volume, Purcell factor, collection efficiency, and far-field / polarization observables\n", "- Cached HDF5 results can be reused when present, so cloud reruns are optional\n", "\n", "### What You Will Learn\n", "\n", "- Generate notebook-local cavity geometry for simulation\n", "- Run a broadband scout simulation to locate the cavity resonance\n", "- Refine the response with a narrowband characterization simulation\n", "- Extract Q, mode volume, Purcell factor, collection efficiency, and far-field / polarization observables\n", "\n", "### Device at a Glance\n", "\n", "| Parameter | Value |\n", "|-----------|-------|\n", "| Material | Diamond (n ≈ 2.41 @ 640 nm) |\n", "| Slab thickness | 136 nm |\n", "| Target wavelength | ~615–645 nm |\n", "| Sidewall angle | 15.6° (fabrication taper) |\n", "| Cavity type | Nanobeam photonic crystal |" ] }, { "cell_type": "markdown", "id": "02-background", "metadata": {}, "source": [ "## Background\n", "\n", "Diamond photonic crystal nanobeam cavities are a useful platform for **solid-state quantum photonics** because they can combine small optical mode volumes with high quality factors, making them attractive for enhancing light-matter interaction with color centers such as **SnV** through the **Purcell effect**:\n", "\n", "$$F_P = \\frac{3}{4\\pi^2} \\left(\\frac{\\lambda_0}{n}\\right)^3 \\frac{Q}{V_{\\text{eff}}}$$\n", "\n", "where $Q$ is the quality factor, $V_{\\text{eff}}$ the effective mode volume, $\\lambda_0$ the free-space resonance wavelength, and $n$ the refractive index at the emitter site.\n", "\n", "In this notebook, we take one fabricated cavity instance, treat its measured thickness, sidewall angle, cavity outline, and hole pattern as fixed inputs, and resolve its optical response with a two-stage FDTD workflow.\n", "\n", "The emphasis here is the reusable simulation procedure itself: generate a fabrication-aware cavity geometry inside the notebook, identify the resonance with a broadband scout run, and then characterize the mode in detail with a narrowband simulation. The same workflow can also be embedded in larger SEM-based fabrication-analysis pipelines, but that broader context is secondary in this notebook.\n", "\n", "### Simulation Workflow\n", "\n", "We use a **two-stage FDTD approach** to balance computational cost with accuracy:\n", "\n", "| Stage | Bandwidth | Monitors | Purpose |\n", "|-------|-----------|----------|---------|\n", "| **Scout** | 12 % (broadband) | point probe | Locate resonance; extract Q and decay time |\n", "| **Lock-in** | 2 % (narrowband) | near-field, 3-D field, far-field | Mode volume, collection efficiency, polarization |\n", "\n", "The scout stage uses a short, broad Gaussian pulse to excite all cavity modes in the spectral window; the time-domain ringdown is decomposed into decaying sinusoids using Prony's method (Tidy3D [ResonanceFinder](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.plugins.resonance.ResonanceFinder.html)). The detected resonance wavelength then seeds the lock-in stage, which resolves the cavity mode in detail." ] }, { "cell_type": "markdown", "id": "03-toc", "metadata": {}, "source": [ "## Table of Contents\n", "\n", "1. [Imports and Configuration](#imports)\n", "2. [Define the Diamond Material Model](#material)\n", "3. [Generate Local Cavity Geometry](#geometry)\n", "4. [Run a Broadband Resonance Search](#scout)\n", "5. [Extract the Cavity Q-Factor](#qfactor)\n", "6. [Run a Narrowband Characterization Simulation](#lockin)\n", "7. [Near-Field Mode Analysis](#nearfield)\n", "8. [Mode Volume and Purcell Enhancement](#modevolume)\n", "9. [Far-Field Radiation and Collection Efficiency](#farfield)\n", "10. [Polarization Analysis](#polarization)\n", "11. [Results Summary](#summary)\n", "\n", "\"diagram\"" ] }, { "cell_type": "markdown", "id": "04-imports-header", "metadata": {}, "source": [ "\n", "## 1. Imports and Configuration" ] }, { "cell_type": "code", "execution_count": 1, "id": "05-imports", "metadata": { "execution": { "iopub.execute_input": "2026-04-07T18:23:03.414663Z", "iopub.status.busy": "2026-04-07T18:23:03.414444Z", "iopub.status.idle": "2026-04-07T18:23:05.168281Z", "shell.execute_reply": "2026-04-07T18:23:05.168036Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "tidy3d version: 2.10.2\n" ] } ], "source": [ "import warnings\n", "from pathlib import Path\n", "\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import matplotlib.patches as mpatches\n", "from matplotlib.colors import LinearSegmentedColormap\n", "from cycler import cycler\n", "from scipy.optimize import curve_fit\n", "from scipy.signal import hilbert\n", "\n", "import tidy3d as td\n", "from tidy3d.plugins.resonance import ResonanceFinder\n", "import gdstk\n", "\n", "warnings.filterwarnings(\"ignore\")\n", "\n", "print(f\"tidy3d version: {td.__version__}\")" ] }, { "cell_type": "code", "execution_count": 2, "id": "06-config", "metadata": { "execution": { "iopub.execute_input": "2026-04-07T18:23:05.169725Z", "iopub.status.busy": "2026-04-07T18:23:05.169551Z", "iopub.status.idle": "2026-04-07T18:23:05.173350Z", "shell.execute_reply": "2026-04-07T18:23:05.173160Z" } }, "outputs": [], "source": [ "# ── Physical parameters ───────────────────────────────────────────────────────\n", "THICKNESS_UM = 0.136 # Diamond slab thickness [µm]\n", "WAVELENGTH_SCOUT = 0.630 # Broadband centre wavelength [µm] (630 nm)\n", "SIDEWALL_ANGLE_DEG = 15.6 # Fabrication sidewall angle [degrees]\n", "NA = 0.65 # Collection numerical aperture\n", "N_BG = 1.0 # Background refractive index (air)\n", "C0 = 299_792_458.0 # Speed of light [m/s]\n", "\n", "# ── Notebook-local runtime paths and embedded geometry ──────────────────────\n", "# This notebook can generate its own GDS files from explicit cavity and hole\n", "# specs, so no external geometry files are required.\n", "RUNTIME_ROOT = Path.cwd().resolve() / \"tidy3d_diamond_photonic_cavity_runtime\"\n", "GDS_DIR = RUNTIME_ROOT / \"gds\"\n", "RESULTS_DIR = RUNTIME_ROOT / \"data\" / \"results\"\n", "GDS_DIR.mkdir(parents=True, exist_ok=True)\n", "RESULTS_DIR.mkdir(parents=True, exist_ok=True)\n", "\n", "CAVITY_BBOX_UM = (-15.675, -0.157, 15.675, 0.157)\n", "HOLE_X_POSITIONS_UM = np.array(\n", " [\n", " -9.831,\n", " -9.63,\n", " -9.429,\n", " -9.228,\n", " -9.027,\n", " -8.826,\n", " -8.625,\n", " -8.424,\n", " -8.223,\n", " -8.022,\n", " -7.821,\n", " -7.62,\n", " -7.4195,\n", " -7.2185,\n", " -7.0175,\n", " -6.8165,\n", " -6.6165,\n", " -6.4175,\n", " -6.2185,\n", " -6.0215,\n", " -5.8265,\n", " -5.6325,\n", " -5.4405,\n", " -5.2495,\n", " -5.0605,\n", " -4.8715,\n", " -4.6705,\n", " -4.4695,\n", " -4.2685,\n", " -4.0675,\n", " -3.8665,\n", " -3.6655,\n", " -3.4645,\n", " -3.2635,\n", " -3.0625,\n", " -2.8615,\n", " -2.6605,\n", " -2.4595,\n", " -2.2585,\n", " -2.058,\n", " -1.8575,\n", " -1.6575,\n", " -1.4575,\n", " -1.2595,\n", " -1.0625,\n", " -0.8665,\n", " -0.6725,\n", " -0.4805,\n", " -0.2905,\n", " -0.101,\n", " 0.102,\n", " 0.2915,\n", " 0.4815,\n", " 0.6735,\n", " 0.8675,\n", " 1.0635,\n", " 1.2605,\n", " 1.4585,\n", " 1.6585,\n", " 1.8585,\n", " 2.0585,\n", " 2.2595,\n", " 2.4605,\n", " 2.6615,\n", " 2.8625,\n", " 3.0635,\n", " 3.2645,\n", " 3.4655,\n", " 3.6665,\n", " 3.8675,\n", " 4.0685,\n", " 4.2695,\n", " 4.4705,\n", " 4.6715,\n", " 4.8725,\n", " 5.0615,\n", " 5.2505,\n", " 5.4415,\n", " 5.6335,\n", " 5.827,\n", " 6.0225,\n", " 6.2195,\n", " 6.4185,\n", " 6.6175,\n", " 6.8175,\n", " 7.0185,\n", " 7.2195,\n", " 7.42,\n", " 7.6205,\n", " 7.8215,\n", " 8.0225,\n", " 8.2235,\n", " 8.4245,\n", " 8.6255,\n", " 8.8265,\n", " 9.0275,\n", " 9.2285,\n", " 9.4295,\n", " 9.6305,\n", " 9.8315,\n", " ],\n", " dtype=float,\n", ")\n", "HOLE_RADIUS_X_UM = np.full(HOLE_X_POSITIONS_UM.shape, 0.0425, dtype=float)\n", "HOLE_RADIUS_X_UM[[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 39, 49, 50, 79, 87]] = 0.043\n", "HOLE_CENTER_Y_UM = 0.0\n", "HOLE_RADIUS_Y_UM = 0.043\n", "ELLIPSE_TOLERANCE_UM = 0.001\n", "# This reproduces the cavity bounding box and the explicit hole centre/radius\n", "# specification in a self-contained way, but it does not preserve the exact\n", "# legacy polygonization of the original fabricated GDS files byte-for-byte.\n", "\n", "\n", "def generate_local_gds_from_specs(\n", " force: bool = False, subtract_holes: bool = True\n", ") -> tuple[Path, Path]:\n", " cavity_path = GDS_DIR / \"Cavity_Fab.gds\"\n", " holes_path = GDS_DIR / \"Holes_Fab.gds\"\n", " if cavity_path.exists() and holes_path.exists() and not force:\n", " return cavity_path, holes_path\n", "\n", " holes = [\n", " gdstk.ellipse(\n", " (float(x_um), HOLE_CENTER_Y_UM),\n", " (float(rx_um), HOLE_RADIUS_Y_UM),\n", " tolerance=ELLIPSE_TOLERANCE_UM,\n", " layer=0,\n", " datatype=0,\n", " )\n", " for x_um, rx_um in zip(HOLE_X_POSITIONS_UM, HOLE_RADIUS_X_UM)\n", " ]\n", "\n", " holes_lib = gdstk.Library(unit=1e-6, precision=1e-9)\n", " holes_top = gdstk.Cell(\"TOP\")\n", " for hole in holes:\n", " holes_top.add(hole)\n", " holes_lib.add(holes_top)\n", " holes_lib.write_gds(str(holes_path))\n", "\n", " xmin, ymin, xmax, ymax = CAVITY_BBOX_UM\n", " rect = gdstk.rectangle((xmin, ymin), (xmax, ymax), layer=0, datatype=0)\n", " cavity_polys = gdstk.boolean([rect], holes, \"not\") if subtract_holes else [rect]\n", "\n", " cavity_lib = gdstk.Library(unit=1e-6, precision=1e-9)\n", " cavity_top = gdstk.Cell(\"TOP\")\n", " for poly in cavity_polys or [rect]:\n", " cavity_top.add(poly)\n", " cavity_lib.add(cavity_top)\n", " cavity_lib.write_gds(str(cavity_path))\n", " return cavity_path, holes_path\n", "\n", "\n", "CAVITY_GDS, HOLES_GDS = generate_local_gds_from_specs(force=False, subtract_holes=True)\n", "\n", "# ── Cached analysis-grade results ────────────────────────────────────────────\n", "# In one-file mode these paths live under the notebook runtime directory.\n", "SCOUT_RESULTS = RESULTS_DIR / \"results_scout_q_only_0.136um_reference.hdf5\"\n", "LOCKIN_RESULTS = RESULTS_DIR / \"results_lockin_full_0.136um.hdf5\"\n", "\n", "# ── Simulation profile ───────────────────────────────────────────────────────\n", "# `analysis_full` keeps the full geometry and monitor suite, with the scout\n", "# runtime reduced to 6 ps for cheaper manual reruns.\n", "# `sobol_cheap` follows the low-cost Sobol scout philosophy: 620 nm centre,\n", "# 7 ps runtime, 13 steps/wavelength, and it skips rerunning the expensive\n", "# lock-in stage unless you force it back on below.\n", "SIMULATION_PROFILE = \"analysis_full\" # 'analysis_full' or 'sobol_cheap'\n", "FORCE_RUN_LOCKIN_STAGE = False\n", "\n", "PROFILE_CONFIG = {\n", " \"analysis_full\": dict(\n", " scout_wavelength_um=0.630,\n", " min_steps_per_wvl=18,\n", " scout_run_time_ps=6.0,\n", " lockin_run_time_ps=8.0,\n", " scout_bandwidth_rel=0.12,\n", " lockin_bandwidth_rel=0.02,\n", " run_lockin_stage=True,\n", " ),\n", " \"sobol_cheap\": dict(\n", " scout_wavelength_um=0.620,\n", " min_steps_per_wvl=13,\n", " scout_run_time_ps=7.0,\n", " lockin_run_time_ps=8.0,\n", " scout_bandwidth_rel=0.12,\n", " lockin_bandwidth_rel=0.02,\n", " run_lockin_stage=False,\n", " ),\n", "}\n", "\n", "if SIMULATION_PROFILE not in PROFILE_CONFIG:\n", " raise ValueError(\n", " f\"Unknown SIMULATION_PROFILE={SIMULATION_PROFILE!r}. \"\n", " f\"Choose from {tuple(PROFILE_CONFIG)}.\"\n", " )\n", "\n", "PROFILE = PROFILE_CONFIG[SIMULATION_PROFILE]\n", "SCOUT_WAVELENGTH_UM = PROFILE[\"scout_wavelength_um\"]\n", "MIN_STEPS_PER_WVL = PROFILE[\"min_steps_per_wvl\"]\n", "SCOUT_RUN_TIME_PS = PROFILE[\"scout_run_time_ps\"]\n", "LOCKIN_RUN_TIME_PS = PROFILE[\"lockin_run_time_ps\"]\n", "SCOUT_BANDWIDTH = PROFILE[\"scout_bandwidth_rel\"]\n", "LOCKIN_BANDWIDTH = PROFILE[\"lockin_bandwidth_rel\"]\n", "RUN_LOCKIN_STAGE = PROFILE[\"run_lockin_stage\"] or FORCE_RUN_LOCKIN_STAGE\n", "\n", "# Keep all reruns separate so the baseline analysis cache is preserved.\n", "SCOUT_RUN_RESULTS = (\n", " RESULTS_DIR / \"results_scout_analysis_full_0.136um.hdf5\"\n", " if SIMULATION_PROFILE == \"analysis_full\"\n", " else RESULTS_DIR / \"results_scout_sobol_like_0.136um.hdf5\"\n", ")\n", "LOCKIN_RUN_RESULTS = (\n", " RESULTS_DIR / \"results_lockin_analysis_full_0.136um.hdf5\"\n", " if SIMULATION_PROFILE == \"analysis_full\"\n", " else RESULTS_DIR / \"results_lockin_sobol_like_0.136um.hdf5\"\n", ")\n", "SCOUT_TASK_NAME = f\"diamond_cavity_scout_{SIMULATION_PROFILE}\"\n", "LOCKIN_TASK_NAME = f\"diamond_cavity_lockin_{SIMULATION_PROFILE}\"\n", "\n", "# ── Simulation domain parameters ─────────────────────────────────────────────\n", "# The nanobeam GDS spans ~31 µm; only the central 21 µm is simulated —\n", "# 5 µm is trimmed from each x-end (mirror terminations) to reduce cost.\n", "# y and z are padded with 2 µm PML buffers on each side.\n", "X_TRIM_UM = 5.0 # µm trimmed from each long-axis end of the GDS\n", "PML_PAD_UM = 2.0 # µm PML buffer on y/z faces\n", "PML_LAYERS = 12 # PML absorption layers (12 for high-Q accuracy)\n", "\n", "# ── Execution flag ────────────────────────────────────────────────────────────\n", "# When True, the selected profile is submitted to Tidy3D cloud.\n", "# `sobol_cheap` reruns only Stage 1 by default and reuses the lock-in cache.\n", "# When False, the notebook loads the stable baseline cached HDF5 results.\n", "RUN_SIMULATIONS = True\n", "PREFER_RERUN_RESULTS = False\n", "SCOUT_LOAD_RESULTS = (\n", " SCOUT_RUN_RESULTS\n", " if RUN_SIMULATIONS or (PREFER_RERUN_RESULTS and SCOUT_RUN_RESULTS.exists())\n", " else SCOUT_RESULTS\n", ")\n", "LOCKIN_LOAD_RESULTS = (\n", " LOCKIN_RUN_RESULTS\n", " if RUN_SIMULATIONS or (PREFER_RERUN_RESULTS and LOCKIN_RUN_RESULTS.exists())\n", " else LOCKIN_RESULTS\n", ")" ] }, { "cell_type": "code", "execution_count": 3, "id": "07-style", "metadata": { "execution": { "iopub.execute_input": "2026-04-07T18:23:05.174448Z", "iopub.status.busy": "2026-04-07T18:23:05.174378Z", "iopub.status.idle": "2026-04-07T18:23:05.176726Z", "shell.execute_reply": "2026-04-07T18:23:05.176537Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Plot theme ready.\n" ] } ], "source": [ "# ── Publication-style plot theme ──────────────────────────────────────────────\n", "RED = \"#840032\"\n", "BLUE = \"#002642\"\n", "YELLOW = \"#e59500\"\n", "WHITE = \"#FFFAF2\"\n", "BLACK = \"#02040f\"\n", "PALETTE = [RED, BLUE, YELLOW, BLACK]\n", "\n", "# Monopolar colormap: black → blue → red → yellow → white\n", "mono_cmap = LinearSegmentedColormap.from_list(\n", " \"mono\", [BLACK, BLUE, RED, YELLOW, WHITE], N=256\n", ")\n", "# Bipolar colormap: black → blue → white → yellow → red\n", "bipolar_cmap = LinearSegmentedColormap.from_list(\n", " \"bipolar\", [BLACK, BLUE, WHITE, YELLOW, RED], N=256\n", ")\n", "\n", "plt.style.use(\"seaborn-v0_8-whitegrid\")\n", "plt.rcParams[\"axes.prop_cycle\"] = cycler(color=PALETTE)\n", "plt.rcParams[\"figure.autolayout\"] = True\n", "plt.rcParams[\"font.size\"] = 11\n", "\n", "print(\"Plot theme ready.\")" ] }, { "cell_type": "markdown", "id": "08-material-header", "metadata": {}, "source": [ "\n", "## 2. Define the Diamond Material Model\n", "\n", "Diamond's refractive index is described by a two-term **Sellmeier equation** (Zaitsev 2001):\n", "\n", "$$n^2(\\lambda) = 1 + \\frac{B_1 \\lambda^2}{\\lambda^2 - C_1} + \\frac{B_2 \\lambda^2}{\\lambda^2 - C_2}$$\n", "\n", "with $B_1 = 0.3306$, $C_1 = 0.175^2\\,\\mu\\text{m}^2$, $B_2 = 4.3356$, $C_2 = 0.106^2\\,\\mu\\text{m}^2$. The model is valid from 0.23 to 5 µm." ] }, { "cell_type": "code", "execution_count": 4, "id": "09-diamond", "metadata": { "execution": { "iopub.execute_input": "2026-04-07T18:23:05.177760Z", "iopub.status.busy": "2026-04-07T18:23:05.177706Z", "iopub.status.idle": "2026-04-07T18:23:05.281997Z", "shell.execute_reply": "2026-04-07T18:23:05.281767Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "n_diamond at 630 nm : 2.4125\n" ] }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "def n_diamond(wavelength_um):\n", " \"\"\"\n", " Diamond refractive index via the two-term Sellmeier equation.\n", "\n", " Reference: Zaitsev, Optical Properties of Diamond (2001).\n", " Valid range: 0.23 – 5 µm.\n", "\n", " Parameters\n", " ----------\n", " wavelength_um : float — free-space wavelength [µm]\n", "\n", " Returns\n", " -------\n", " float — refractive index at the given wavelength\n", " \"\"\"\n", " lam2 = np.asarray(wavelength_um, dtype=float) ** 2\n", " B1, C1 = 0.3306, 0.175**2 # first oscillator\n", " B2, C2 = 4.3356, 0.106**2 # second oscillator\n", " n2 = 1.0 + B1 * lam2 / (lam2 - C1) + B2 * lam2 / (lam2 - C2)\n", " return np.sqrt(n2)\n", "\n", "\n", "# Tidy3D dispersive medium (Sellmeier coefficients)\n", "diamond_medium = td.Sellmeier(coeffs=[(0.3306, 0.175**2), (4.3356, 0.106**2)])\n", "air_medium = td.Medium(permittivity=1.0)\n", "\n", "n_scout = float(n_diamond(SCOUT_WAVELENGTH_UM))\n", "print(f\"n_diamond at {SCOUT_WAVELENGTH_UM * 1e3:.0f} nm : {n_scout:.4f}\")\n", "\n", "# ── Dispersion curve ──────────────────────────────────────────────────────────\n", "wl = np.linspace(0.45, 1.0, 400)\n", "n_wl = n_diamond(wl)\n", "\n", "fig, ax = plt.subplots(figsize=(5.5, 3.5))\n", "ax.plot(wl * 1e3, n_wl, color=RED, lw=2, label=\"Diamond\")\n", "ax.axvline(\n", " SCOUT_WAVELENGTH_UM * 1e3,\n", " color=BLUE,\n", " ls=\"--\",\n", " lw=1.5,\n", " label=f\"{SCOUT_WAVELENGTH_UM * 1e3:.0f} nm (n = {n_scout:.3f})\",\n", ")\n", "ax.set_xlabel(\"Wavelength (nm)\")\n", "ax.set_ylabel(\"Refractive index $n$\")\n", "ax.set_title(\"Diamond Dispersion (Sellmeier)\")\n", "ax.legend(framealpha=0.85)\n", "ax.set_xlim(450, 1000)\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "10-geometry-header", "metadata": {}, "source": [ "\n", "## 3. Generate Local Cavity Geometry\n", "\n", "The notebook generates two local GDS files from embedded cavity and hole specifications:\n", "\n", "- **`Cavity_Fab.gds`** — the diamond nanobeam outline written from the embedded cavity specification\n", "- **`Holes_Fab.gds`** — the photonic crystal hole array written from the embedded hole positions and radii\n", "\n", "The slab is extruded with the measured fabrication **sidewall angle of 15.6°**, producing a trapezoidal cross-section that models the etched nanobeam profile. The photonic-crystal holes are kept **vertical** through the slab, matching the geometry used in the shared simulation scripts.\n", "\n", "> **Note:** All coordinates are in micrometres (µm); Tidy3D uses µm as its default length unit. The code cell below generates the local GDS files, stores them in a notebook-local runtime folder, and then builds the Tidy3D geometry from those files. The bundle `gds/` folder also contains `*_reference.gds` and `*_generated.gds` copies for direct comparison.\n" ] }, { "cell_type": "code", "execution_count": 5, "id": "11-geometry-load", "metadata": { "execution": { "iopub.execute_input": "2026-04-07T18:23:05.283533Z", "iopub.status.busy": "2026-04-07T18:23:05.283421Z", "iopub.status.idle": "2026-04-07T18:23:05.321675Z", "shell.execute_reply": "2026-04-07T18:23:05.321436Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "======================================================================\n", "SETTING UP GEOMETRY PARAMETERS\n", "======================================================================\n", "✓ Geometry parameters loaded\n", " - Cavity thickness: 0.136 µm\n", " - Wavelength: 0.63 µm\n", " - Optimized padding: x=[-5.0, -5.0], y=[2.0, 2.0], z=[2.0, 2.0]\n", "\n", "Creating diamond medium with full Sellmeier dispersion...\n", " - Using full Sellmeier dispersion model\n", " - Coefficients: B1=0.3306, C1=0.175²; B2=4.3356, C2=0.106²\n", " - Reference n(λ=0.630 µm) = 2.412500\n", " - Full frequency-dependent dispersion enabled for FDTD simulation\n", "\n", "Generating local geometry from embedded cavity and hole specifications...\n", " - Using cell: TOP\n", " - Original bbox: x[-15.675, 15.675], y[-0.157, 0.157]\n", " - Geometry bbox: x[-15.675, 15.675], y[-0.157, 0.157]\n", " - Simulation domain: left=-10.675, right=10.675, bottom=-2.157, top=2.157\n", " - X truncation: 31.350 → 21.350 µm (reduced by 10.000 µm)\n", " - Simulation size: (21.350, 4.314, 4.136) µm\n", " - Simulation center: (0.000, 0.000, 0.000) µm\n", "\n", "Creating core structure...\n", " - Trapezoidal core (native sidewall): sidewall_angle=15.60°\n", " - Bottom width (x × y): 31.350 µm × 0.314 µm\n", " - Top width (x × y): 31.274 µm × 0.238 µm\n", " - Thickness: 0.136 µm\n", "\n", "Creating hole structures...\n", " - Using holes cell: TOP\n", " - Imported 100 hole polygon(s)\n", " - Created 1 hole structure(s)\n", " - Thickness: 0.136 µm\n", "Cavity outline : x=[-15.675, 15.675] µm, y=[-0.157, 0.157] µm\n", "Hole groups : 1 structure(s)\n", "Cavity centre : (0.0000, 0.0000) µm\n" ] } ], "source": [ "from typing import Dict, List, Optional, Tuple\n", "\n", "\n", "def find_simulations_root() -> Path:\n", " \"\"\"Locate a repo-style root when present, otherwise fall back to the notebook runtime root.\"\"\"\n", " cwd = Path.cwd().resolve()\n", " for candidate in [cwd, *cwd.parents]:\n", " if (candidate / \"gds\").exists():\n", " return candidate\n", " if (candidate / \"Simulations\" / \"gds\").exists():\n", " return candidate / \"Simulations\"\n", " return RUNTIME_ROOT\n", "\n", "\n", "class NotebookSimulationSetup:\n", " \"\"\"Self-contained notebook copy of the shared simulation setup logic.\"\"\"\n", "\n", " def __init__(\n", " self,\n", " thickness_um: float = 0.14,\n", " wavelength_um: float = 0.62,\n", " source_bandwidth_rel: float = 0.12,\n", " cavity_gds: Optional[str] = None,\n", " holes_gds: Optional[str] = None,\n", " sidewall_angle_deg: float = 0.0,\n", " trapezoid_slices: int = 1,\n", " ):\n", " self.thickness_um = thickness_um\n", " self.wavelength_um = wavelength_um\n", " self.source_bandwidth_rel = source_bandwidth_rel\n", " self._cavity_gds_input = cavity_gds\n", " self._holes_gds_input = holes_gds\n", " self.params = self._setup_geometry_parameters()\n", " self.sidewall_angle_deg = float(sidewall_angle_deg or 0.0)\n", " self.trapezoid_slices = int(trapezoid_slices or 1)\n", " self.diamond_medium, self.clad_medium, self.f0_center = (\n", " self._create_diamond_medium()\n", " )\n", "\n", " def _setup_geometry_parameters(self) -> Dict:\n", " print(\"=\" * 70)\n", " print(\"SETTING UP GEOMETRY PARAMETERS\")\n", " print(\"=\" * 70)\n", "\n", " repo_root = find_simulations_root()\n", "\n", " if self._cavity_gds_input:\n", " cav_candidate = Path(self._cavity_gds_input)\n", " gds_path = (\n", " cav_candidate\n", " if cav_candidate.is_absolute()\n", " else (repo_root / \"gds\" / cav_candidate.name)\n", " )\n", " gds_path = gds_path.resolve()\n", " else:\n", " gds_path = (repo_root / \"gds\" / \"Cavity_Design.gds\").resolve()\n", "\n", " if self._holes_gds_input:\n", " hol_candidate = Path(self._holes_gds_input)\n", " hole_gds_path = (\n", " hol_candidate\n", " if hol_candidate.is_absolute()\n", " else (repo_root / \"gds\" / hol_candidate.name)\n", " )\n", " hole_gds_path = hole_gds_path.resolve()\n", " else:\n", " hole_gds_path = (repo_root / \"gds\" / \"Holes_Design.gds\").resolve()\n", "\n", " pad_x_neg = -5.0\n", " pad_x_pos = -5.0\n", " pad_y_neg = 2.0\n", " pad_y_pos = 2.0\n", " pad_z_neg = 2.0\n", " pad_z_pos = 2.0\n", "\n", " n_clad = 1.0\n", " chunk_max = 100\n", " top_cell_name = \"TOP\"\n", " hole_layer = (0, 0)\n", "\n", " print(\"✓ Geometry parameters loaded\")\n", " print(f\" - Cavity thickness: {self.thickness_um} µm\")\n", " print(f\" - Wavelength: {self.wavelength_um} µm\")\n", " print(\n", " f\" - Optimized padding: x=[{pad_x_neg}, {pad_x_pos}], \"\n", " f\"y=[{pad_y_neg}, {pad_y_pos}], z=[{pad_z_neg}, {pad_z_pos}]\"\n", " )\n", "\n", " return {\n", " \"gds_path\": gds_path,\n", " \"hole_gds_path\": hole_gds_path,\n", " \"thickness_um\": self.thickness_um,\n", " \"wavelength_um\": self.wavelength_um,\n", " \"pad_x_neg\": pad_x_neg,\n", " \"pad_x_pos\": pad_x_pos,\n", " \"pad_y_neg\": pad_y_neg,\n", " \"pad_y_pos\": pad_y_pos,\n", " \"pad_z_neg\": pad_z_neg,\n", " \"pad_z_pos\": pad_z_pos,\n", " \"n_clad\": n_clad,\n", " \"chunk_max\": chunk_max,\n", " \"top_cell_name\": top_cell_name,\n", " \"hole_layer\": hole_layer,\n", " }\n", "\n", " def _create_diamond_medium(self) -> Tuple[td.Medium, td.Medium, float]:\n", " print(\"\\nCreating diamond medium with full Sellmeier dispersion...\")\n", "\n", " def n_diamond_sellmeier(lambda_um: float) -> float:\n", " lam2 = float(lambda_um) ** 2\n", " B1, C1 = 0.3306, 0.175**2\n", " B2, C2 = 4.3356, 0.106**2\n", " n2 = 1.0 + B1 * lam2 / (lam2 - C1) + B2 * lam2 / (lam2 - C2)\n", " return float(np.sqrt(n2))\n", "\n", " n_diamond_ref = n_diamond_sellmeier(self.wavelength_um)\n", "\n", " print(\" - Using full Sellmeier dispersion model\")\n", " print(\" - Coefficients: B1=0.3306, C1=0.175²; B2=4.3356, C2=0.106²\")\n", " print(f\" - Reference n(λ={self.wavelength_um:.3f} µm) = {n_diamond_ref:.6f}\")\n", " print(\" - Full frequency-dependent dispersion enabled for FDTD simulation\")\n", "\n", " sellmeier_coeffs = [\n", " (0.3306, 0.175**2),\n", " (4.3356, 0.106**2),\n", " ]\n", "\n", " diamond_medium = td.Sellmeier(coeffs=sellmeier_coeffs)\n", " clad_medium = td.Medium(permittivity=1.0)\n", " f0_center = C0 / (self.wavelength_um * 1e-6)\n", " return diamond_medium, clad_medium, f0_center\n", "\n", " @staticmethod\n", " def diamond_n_sellmeier(lambda_um: float) -> float:\n", " lam2 = float(lambda_um) ** 2\n", " B1, C1 = 0.3306, 0.175**2\n", " B2, C2 = 4.3356, 0.106**2\n", " n2 = 1.0 + B1 * lam2 / (lam2 - C1) + B2 * lam2 / (lam2 - C2)\n", " return float(np.sqrt(n2))\n", "\n", " def extract_geometry_from_gds(self) -> Dict:\n", " print(\n", " \"\\nGenerating local geometry from embedded cavity and hole specifications...\"\n", " )\n", "\n", " lib = gdstk.read_gds(str(self.params[\"gds_path\"]))\n", " gds_scale = lib.unit / 1e-6\n", "\n", " tops = lib.top_level()\n", " if not tops:\n", " raise RuntimeError(\"No top-level cells found in GDS.\")\n", " top_cell = tops[0]\n", "\n", " print(f\" - Using cell: {top_cell.name}\")\n", "\n", " bbox = top_cell.bounding_box()\n", " if bbox is None:\n", " raise RuntimeError(\"Could not determine bounding box.\")\n", "\n", " xmin_orig, ymin_orig = np.array(bbox[0]) * gds_scale\n", " xmax_orig, ymax_orig = np.array(bbox[1]) * gds_scale\n", "\n", " print(\n", " f\" - Original bbox: x[{xmin_orig:.3f}, {xmax_orig:.3f}], y[{ymin_orig:.3f}, {ymax_orig:.3f}]\"\n", " )\n", "\n", " xmin = xmin_orig\n", " xmax = xmax_orig\n", " ymin = ymin_orig\n", " ymax = ymax_orig\n", "\n", " left = xmin - self.params[\"pad_x_neg\"]\n", " right = xmax + self.params[\"pad_x_pos\"]\n", " bottom = ymin - self.params[\"pad_y_neg\"]\n", " top = ymax + self.params[\"pad_y_pos\"]\n", " down = -self.params[\"thickness_um\"] / 2 - self.params[\"pad_z_neg\"]\n", " up = self.params[\"thickness_um\"] / 2 + self.params[\"pad_z_pos\"]\n", "\n", " print(\n", " f\" - Geometry bbox: x[{xmin:.3f}, {xmax:.3f}], y[{ymin:.3f}, {ymax:.3f}]\"\n", " )\n", " print(\n", " f\" - Simulation domain: left={left:.3f}, right={right:.3f}, bottom={bottom:.3f}, top={top:.3f}\"\n", " )\n", " print(\n", " f\" - X truncation: {xmax_orig - xmin_orig:.3f} → {right - left:.3f} µm \"\n", " f\"(reduced by {xmax_orig - xmin_orig - (right - left):.3f} µm)\"\n", " )\n", "\n", " size_x = right - left\n", " size_y = top - bottom\n", " size_z = up - down\n", " cx = (left + right) / 2\n", " cy = (bottom + top) / 2\n", " cz = 0.0\n", "\n", " print(f\" - Simulation size: ({size_x:.3f}, {size_y:.3f}, {size_z:.3f}) µm\")\n", " print(f\" - Simulation center: ({cx:.3f}, {cy:.3f}, {cz:.3f}) µm\")\n", "\n", " return {\n", " \"xmin\": xmin,\n", " \"xmax\": xmax,\n", " \"ymin\": ymin,\n", " \"ymax\": ymax,\n", " \"size_x\": size_x,\n", " \"size_y\": size_y,\n", " \"size_z\": size_z,\n", " \"cx\": cx,\n", " \"cy\": cy,\n", " \"cz\": cz,\n", " }\n", "\n", " def create_core_structure(self, geom_params: Dict) -> td.Structure:\n", " print(\"\\nCreating core structure...\")\n", "\n", " rect_vertices = [\n", " (geom_params[\"xmin\"], geom_params[\"ymin\"]),\n", " (geom_params[\"xmin\"], geom_params[\"ymax\"]),\n", " (geom_params[\"xmax\"], geom_params[\"ymax\"]),\n", " (geom_params[\"xmax\"], geom_params[\"ymin\"]),\n", " ]\n", "\n", " if self.sidewall_angle_deg <= 0.0:\n", " core_geo = td.PolySlab(\n", " vertices=rect_vertices,\n", " axis=2,\n", " slab_bounds=(\n", " -self.params[\"thickness_um\"] / 2,\n", " self.params[\"thickness_um\"] / 2,\n", " ),\n", " reference_plane=\"middle\",\n", " )\n", " core_struct = td.Structure(geometry=core_geo, medium=self.diamond_medium)\n", " print(f\" - Core rectangle created with {len(rect_vertices)} vertices\")\n", " print(f\" - Thickness: {self.params['thickness_um']} µm\")\n", " return core_struct\n", "\n", " t = float(self.params[\"thickness_um\"])\n", " angle_rad = float(np.deg2rad(self.sidewall_angle_deg))\n", " width_x = geom_params[\"xmax\"] - geom_params[\"xmin\"]\n", " width_y = geom_params[\"ymax\"] - geom_params[\"ymin\"]\n", " inset_top = t * np.tan(angle_rad)\n", " if 2 * inset_top >= min(width_x, width_y):\n", " print(\"⚠️ Sidewall angle too large for given footprint; reducing to fit.\")\n", " max_inset = 0.49 * min(width_x, width_y)\n", " angle_rad = np.arctan(max_inset / t)\n", " inset_top = t * np.tan(angle_rad)\n", "\n", " core_geo = td.PolySlab(\n", " vertices=rect_vertices,\n", " axis=2,\n", " slab_bounds=(-t / 2, t / 2),\n", " reference_plane=\"bottom\",\n", " sidewall_angle=angle_rad,\n", " )\n", " core_struct = td.Structure(geometry=core_geo, medium=self.diamond_medium)\n", "\n", " top_width_x = max(width_x - 2 * inset_top, 0.0)\n", " top_width_y = max(width_y - 2 * inset_top, 0.0)\n", " print(\n", " f\" - Trapezoidal core (native sidewall): sidewall_angle={self.sidewall_angle_deg:.2f}°\"\n", " )\n", " print(f\" - Bottom width (x × y): {width_x:.3f} µm × {width_y:.3f} µm\")\n", " print(f\" - Top width (x × y): {top_width_x:.3f} µm × {top_width_y:.3f} µm\")\n", " print(f\" - Thickness: {t} µm\")\n", " return core_struct\n", "\n", " def create_hole_structures(self, geom_params: Dict) -> List[td.Structure]:\n", " print(\"\\nCreating hole structures...\")\n", "\n", " lib_holes = gdstk.read_gds(str(self.params[\"hole_gds_path\"]))\n", " gds_scale_holes = lib_holes.unit / 1e-6\n", "\n", " tops_h = lib_holes.top_level()\n", " if not tops_h:\n", " raise RuntimeError(\"No top-level cells found in holes GDS.\")\n", " hole_cell = tops_h[0]\n", "\n", " print(f\" - Using holes cell: {hole_cell.name}\")\n", "\n", " holes_geo = td.Geometry.from_gds(\n", " gds_cell=hole_cell,\n", " gds_layer=self.params[\"hole_layer\"][0],\n", " gds_dtype=self.params[\"hole_layer\"][1],\n", " axis=2,\n", " slab_bounds=(\n", " -self.params[\"thickness_um\"] / 2,\n", " self.params[\"thickness_um\"] / 2,\n", " ),\n", " reference_plane=\"middle\",\n", " gds_scale=gds_scale_holes,\n", " )\n", "\n", " holes_geoms = getattr(holes_geo, \"geometries\", [holes_geo])\n", " hole_structs = []\n", "\n", " for i in range(0, len(holes_geoms), self.params[\"chunk_max\"]):\n", " chunk = holes_geoms[i : i + self.params[\"chunk_max\"]]\n", " geom = td.GeometryGroup(geometries=chunk) if len(chunk) > 1 else chunk[0]\n", " hole_structs.append(td.Structure(geometry=geom, medium=self.clad_medium))\n", "\n", " print(f\" - Imported {len(holes_geoms)} hole polygon(s)\")\n", " print(f\" - Created {len(hole_structs)} hole structure(s)\")\n", " print(f\" - Thickness: {self.params['thickness_um']} µm\")\n", " return hole_structs\n", "\n", " def create_sources_and_monitors(\n", " self, geom_params: Dict\n", " ) -> Tuple[td.PointDipole, List[td.Monitor]]:\n", " print(\"\\nCreating sources and monitors...\")\n", "\n", " source = td.PointDipole(\n", " center=(geom_params[\"cx\"], geom_params[\"cy\"], 0.0),\n", " source_time=td.GaussianPulse(\n", " freq0=self.f0_center,\n", " fwidth=self.f0_center * self.source_bandwidth_rel,\n", " ),\n", " polarization=\"Ey\",\n", " )\n", "\n", " print(\n", " f\" - Source: Point dipole at ({geom_params['cx']:.3f}, {geom_params['cy']:.3f}, 0.0)\"\n", " )\n", " print(f\" - Frequency: {self.f0_center / 1e12:.3f} THz\")\n", " print(\n", " f\" - Bandwidth: {self.f0_center * self.source_bandwidth_rel / 1e12:.2f} THz\"\n", " )\n", "\n", " monitors = []\n", " probe_monitor = td.FieldTimeMonitor(\n", " center=(geom_params[\"cx\"], geom_params[\"cy\"], 0.0),\n", " size=(0, 0, 0),\n", " name=\"probe\",\n", " interval=5,\n", " )\n", " monitors.append(probe_monitor)\n", "\n", " flux_monitor = td.FluxMonitor(\n", " center=(geom_params[\"cx\"], geom_params[\"cy\"], 0.0),\n", " size=(geom_params[\"size_x\"] * 0.8, geom_params[\"size_y\"] * 0.8, 0),\n", " freqs=[self.f0_center],\n", " name=\"flux\",\n", " )\n", " monitors.append(flux_monitor)\n", "\n", " field_monitor = td.FieldMonitor(\n", " center=(geom_params[\"cx\"], geom_params[\"cy\"], 0.0),\n", " size=(geom_params[\"size_x\"] * 0.6, geom_params[\"size_y\"] * 0.6, 0),\n", " freqs=[self.f0_center],\n", " name=\"field_near\",\n", " )\n", " monitors.append(field_monitor)\n", "\n", " print(f\" - Created {len(monitors)} basic monitors\")\n", " return source, monitors\n", "\n", " def create_minimal_q_probe(\n", " self, geom_params: Dict\n", " ) -> Tuple[td.PointDipole, List[td.Monitor]]:\n", " print(\"\\nCreating minimal source and Q-probe monitor...\")\n", "\n", " source = td.PointDipole(\n", " center=(geom_params[\"cx\"], geom_params[\"cy\"], 0.0),\n", " source_time=td.GaussianPulse(\n", " freq0=self.f0_center,\n", " fwidth=self.f0_center * self.source_bandwidth_rel,\n", " ),\n", " polarization=\"Ey\",\n", " )\n", "\n", " probe_monitor = td.FieldTimeMonitor(\n", " center=(geom_params[\"cx\"], geom_params[\"cy\"], 0.0),\n", " size=(0, 0, 0),\n", " name=\"probe\",\n", " interval=5,\n", " )\n", "\n", " print(\" - Created minimal monitor set: ['probe']\")\n", " return source, [probe_monitor]\n", "\n", " def create_farfield_monitors(self, geom_params: Dict) -> List[td.Monitor]:\n", " print(\"\\nCreating improved far-field monitors...\")\n", "\n", " monitor_z = 1.5\n", " monitor_size = 8.0\n", " print(f\" - Positioning monitors at z = {monitor_z} µm\")\n", " print(f\" - Monitor size: {monitor_size} µm\")\n", "\n", " monitors = []\n", "\n", " x_samples = np.linspace(-4, 4, 50)\n", " y_samples = np.linspace(-4, 4, 50)\n", " cartesian_monitor = td.FieldProjectionCartesianMonitor(\n", " center=(geom_params[\"cx\"], geom_params[\"cy\"], monitor_z),\n", " size=(monitor_size, monitor_size, 0.0),\n", " freqs=[self.f0_center],\n", " name=\"farfield_cartesian\",\n", " x=x_samples,\n", " y=y_samples,\n", " proj_axis=2,\n", " )\n", " monitors.append(cartesian_monitor)\n", "\n", " u_max = 0.95\n", " ux_samples = np.linspace(-u_max, u_max, 40)\n", " uy_samples = np.linspace(-u_max, u_max, 40)\n", " kspace_monitor = td.FieldProjectionKSpaceMonitor(\n", " center=(geom_params[\"cx\"], geom_params[\"cy\"], monitor_z),\n", " size=(monitor_size, monitor_size, 0.0),\n", " freqs=[self.f0_center],\n", " name=\"farfield_kspace\",\n", " ux=ux_samples,\n", " uy=uy_samples,\n", " proj_axis=2,\n", " )\n", " monitors.append(kspace_monitor)\n", "\n", " theta_deg = np.linspace(0.0, 90.0, 100)\n", " phi_deg = np.linspace(0.0, 360.0, 200)\n", " theta = np.deg2rad(theta_deg)\n", " phi = np.deg2rad(phi_deg)\n", " angle_monitor = td.FieldProjectionAngleMonitor(\n", " center=(geom_params[\"cx\"], geom_params[\"cy\"], monitor_z),\n", " size=(monitor_size, monitor_size, 0.0),\n", " freqs=[self.f0_center],\n", " name=\"farfield_angles\",\n", " theta=theta,\n", " phi=phi,\n", " normal_dir=\"+\",\n", " )\n", " monitors.append(angle_monitor)\n", "\n", " print(f\" - Created {len(monitors)} improved far-field monitors\")\n", " return monitors\n", "\n", " def create_mode_volume_monitor(self, geom_params: Dict) -> td.Monitor:\n", " return td.FieldMonitor(\n", " name=\"fld_3d_box\",\n", " center=(geom_params[\"cx\"], geom_params[\"cy\"], 0.0),\n", " size=(\n", " min(geom_params[\"size_x\"], 6.0),\n", " min(geom_params[\"size_y\"], 3.0),\n", " min(geom_params[\"size_z\"], 2.0),\n", " ),\n", " fields=[\"Ex\", \"Ey\", \"Ez\"],\n", " freqs=[self.f0_center],\n", " interval_space=(1, 1, 1),\n", " )\n", "\n", " def create_simulation(\n", " self, run_time_ps: float = 10.0, min_steps_per_wvl: int = 18\n", " ) -> td.Simulation:\n", " print(\"\\nCreating complete simulation...\")\n", "\n", " geom_params = self.extract_geometry_from_gds()\n", " core_struct = self.create_core_structure(geom_params)\n", " hole_structs = self.create_hole_structures(geom_params)\n", " all_structures = [core_struct] + hole_structs\n", "\n", " source, basic_monitors = self.create_sources_and_monitors(geom_params)\n", " farfield_monitors = self.create_farfield_monitors(geom_params)\n", " modevol_monitor = self.create_mode_volume_monitor(geom_params)\n", " all_monitors = basic_monitors + farfield_monitors + [modevol_monitor]\n", "\n", " run_time = run_time_ps * 1e-12\n", " simulation = td.Simulation(\n", " size=(geom_params[\"size_x\"], geom_params[\"size_y\"], geom_params[\"size_z\"]),\n", " center=(geom_params[\"cx\"], geom_params[\"cy\"], geom_params[\"cz\"]),\n", " grid_spec=td.GridSpec.auto(\n", " min_steps_per_wvl=min_steps_per_wvl, wavelength=self.wavelength_um\n", " ),\n", " structures=all_structures,\n", " sources=[source],\n", " monitors=all_monitors,\n", " run_time=run_time,\n", " boundary_spec=td.BoundarySpec.all_sides(boundary=td.PML()),\n", " )\n", "\n", " print(\"✓ Complete simulation created successfully\")\n", " print(f\" - Structures: {len(all_structures)}\")\n", " print(f\" - Monitors: {len(all_monitors)}\")\n", " print(f\" - Run time: {run_time_ps:.1f} ps\")\n", " print(\n", " f\" - Grid: min_steps_per_wvl={min_steps_per_wvl} with wavelength={self.wavelength_um} µm\"\n", " )\n", " print(\" - Boundaries: PML(8 layers)\")\n", " return simulation\n", "\n", " def create_q_scout_simulation(\n", " self, run_time_ps: float = 10.0, min_steps_per_wvl: int = 18\n", " ) -> td.Simulation:\n", " print(\"\\nCreating minimal scout simulation (Q-only)...\")\n", "\n", " geom_params = self.extract_geometry_from_gds()\n", " core_struct = self.create_core_structure(geom_params)\n", " hole_structs = self.create_hole_structures(geom_params)\n", " all_structures = [core_struct] + hole_structs\n", "\n", " source, probe_monitors = self.create_minimal_q_probe(geom_params)\n", " run_time = run_time_ps * 1e-12\n", " simulation = td.Simulation(\n", " size=(geom_params[\"size_x\"], geom_params[\"size_y\"], geom_params[\"size_z\"]),\n", " center=(geom_params[\"cx\"], geom_params[\"cy\"], geom_params[\"cz\"]),\n", " grid_spec=td.GridSpec.auto(\n", " min_steps_per_wvl=min_steps_per_wvl, wavelength=self.wavelength_um\n", " ),\n", " structures=all_structures,\n", " sources=[source],\n", " monitors=probe_monitors,\n", " run_time=run_time,\n", " boundary_spec=td.BoundarySpec.all_sides(boundary=td.PML()),\n", " )\n", "\n", " print(\"✓ Minimal scout simulation created successfully\")\n", " print(f\" - Structures: {len(all_structures)}\")\n", " print(f\" - Monitors: {len(probe_monitors)} (probe only)\")\n", " print(f\" - Run time: {run_time_ps:.1f} ps\")\n", " print(\n", " f\" - Grid: min_steps_per_wvl={min_steps_per_wvl} with wavelength={self.wavelength_um} µm\"\n", " )\n", " print(\" - Boundaries: PML(8 layers)\")\n", " return simulation\n", "\n", "\n", "GEOMETRY_SETUP = NotebookSimulationSetup(\n", " thickness_um=THICKNESS_UM,\n", " wavelength_um=SCOUT_WAVELENGTH_UM,\n", " source_bandwidth_rel=SCOUT_BANDWIDTH,\n", " cavity_gds=CAVITY_GDS,\n", " holes_gds=HOLES_GDS,\n", " sidewall_angle_deg=SIDEWALL_ANGLE_DEG,\n", ")\n", "\n", "geom_params = GEOMETRY_SETUP.extract_geometry_from_gds()\n", "core_structure = GEOMETRY_SETUP.create_core_structure(geom_params)\n", "hole_structures = GEOMETRY_SETUP.create_hole_structures(geom_params)\n", "structures = [core_structure] + hole_structures\n", "\n", "X0, X1 = geom_params[\"xmin\"], geom_params[\"xmax\"]\n", "Y0, Y1 = geom_params[\"ymin\"], geom_params[\"ymax\"]\n", "CX, CY = geom_params[\"cx\"], geom_params[\"cy\"]\n", "\n", "print(f\"Cavity outline : x=[{X0:.3f}, {X1:.3f}] µm, y=[{Y0:.3f}, {Y1:.3f}] µm\")\n", "print(f\"Hole groups : {len(hole_structures)} structure(s)\")\n", "print(f\"Cavity centre : ({CX:.4f}, {CY:.4f}) µm\")" ] }, { "cell_type": "code", "execution_count": 6, "id": "12-geometry-vis", "metadata": { "execution": { "iopub.execute_input": "2026-04-07T18:23:05.322913Z", "iopub.status.busy": "2026-04-07T18:23:05.322854Z", "iopub.status.idle": "2026-04-07T18:23:05.362200Z", "shell.execute_reply": "2026-04-07T18:23:05.361978Z" } }, "outputs": [ { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# ── Top-view geometry preview ─────────────────────────────────────────────────\n", "lib_vis = gdstk.read_gds(str(HOLES_GDS))\n", "scale_vis = lib_vis.unit / 1e-6\n", "cell_vis = lib_vis.top_level()[0]\n", "\n", "fig, ax = plt.subplots(figsize=(13.2, 4.8))\n", "\n", "# Diamond slab background\n", "rect = plt.Rectangle(\n", " (X0, Y0), X1 - X0, Y1 - Y0, fc=BLUE, ec=\"none\", alpha=0.85, label=\"Diamond slab\"\n", ")\n", "ax.add_patch(rect)\n", "\n", "# Photonic crystal holes\n", "for poly in cell_vis.polygons:\n", " if poly.layer == 0 and poly.datatype == 0:\n", " pts = poly.points * scale_vis\n", " patch = plt.Polygon(pts, closed=True, fc=WHITE, ec=BLACK, lw=0.4)\n", " ax.add_patch(patch)\n", "\n", "# Dipole source position\n", "ax.plot(CX, CY, \"x\", color=RED, ms=8, mew=2, label=\"Dipole source\")\n", "\n", "ax.set_xlim(X0 - 0.4, X1 + 0.4)\n", "ax.set_ylim(Y0 - 0.4, Y1 + 0.4)\n", "ax.set_aspect(\"equal\")\n", "ax.set_xlabel(\"x (µm)\")\n", "ax.set_ylabel(\"y (µm)\")\n", "ax.set_title(\"Photonic Crystal Nanocavity – Top View\")\n", "\n", "legend_handles = [\n", " mpatches.Patch(fc=BLUE, ec=\"none\", alpha=0.85, label=\"Diamond slab\"),\n", " mpatches.Patch(fc=WHITE, ec=BLACK, lw=0.4, label=\"Air holes\"),\n", " plt.Line2D(\n", " [0],\n", " [0],\n", " marker=\"x\",\n", " color=RED,\n", " linestyle=\"None\",\n", " markersize=8,\n", " markeredgewidth=2,\n", " label=\"Dipole source\",\n", " ),\n", "]\n", "fig.legend(\n", " handles=legend_handles,\n", " loc=\"center left\",\n", " bbox_to_anchor=(0.80, 0.5),\n", " fontsize=9,\n", " frameon=True,\n", " borderaxespad=0.0,\n", ")\n", "fig.tight_layout(rect=(0, 0, 0.78, 1))\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "13-scout-header", "metadata": {}, "source": [ "\n", "## 4. Run a Broadband Resonance Search\n", "\n", "A **y-polarized [point dipole](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.PointDipole.html)** placed at the cavity centre drives a Gaussian pulse with a 12 % relative bandwidth, spanning ~580–700 nm. The only monitor is a zero-size **point probe** recording the time-domain field $E_y(t)$ throughout the simulation.\n", "\n", "The simulation domain is enclosed by **[PML (perfectly matched layer)](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.PML.html) boundaries** on all six sides to absorb outgoing radiation without reflections." ] }, { "cell_type": "code", "execution_count": 7, "id": "14-scout-setup", "metadata": { "execution": { "iopub.execute_input": "2026-04-07T18:23:05.363573Z", "iopub.status.busy": "2026-04-07T18:23:05.363503Z", "iopub.status.idle": "2026-04-07T18:23:05.386796Z", "shell.execute_reply": "2026-04-07T18:23:05.386580Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Creating minimal scout simulation (Q-only)...\n", "\n", "Generating local geometry from embedded cavity and hole specifications...\n", " - Using cell: TOP\n", " - Original bbox: x[-15.675, 15.675], y[-0.157, 0.157]\n", " - Geometry bbox: x[-15.675, 15.675], y[-0.157, 0.157]\n", " - Simulation domain: left=-10.675, right=10.675, bottom=-2.157, top=2.157\n", " - X truncation: 31.350 → 21.350 µm (reduced by 10.000 µm)\n", " - Simulation size: (21.350, 4.314, 4.136) µm\n", " - Simulation center: (0.000, 0.000, 0.000) µm\n", "\n", "Creating core structure...\n", " - Trapezoidal core (native sidewall): sidewall_angle=15.60°\n", " - Bottom width (x × y): 31.350 µm × 0.314 µm\n", " - Top width (x × y): 31.274 µm × 0.238 µm\n", " - Thickness: 0.136 µm\n", "\n", "Creating hole structures...\n", " - Using holes cell: TOP\n", " - Imported 100 hole polygon(s)\n", " - Created 1 hole structure(s)\n", " - Thickness: 0.136 µm\n", "\n", "Creating minimal source and Q-probe monitor...\n", " - Created minimal monitor set: ['probe']\n", "✓ Minimal scout simulation created successfully\n", " - Structures: 2\n", " - Monitors: 1 (probe only)\n", " - Run time: 6.0 ps\n", " - Grid: min_steps_per_wvl=18 with wavelength=0.63 µm\n", " - Boundaries: PML(8 layers)\n", "── Scout Simulation ─────────────────────────────────────────────────\n", " Domain : 21.350 × 4.314 × 4.136 µm\n", " Grid cells : 1498 × 164 × 152 = 37,342,144\n", " Run time : 6.0 ps\n", " Excitation : Ey dipole @ 630.0 nm, Δf/f = 12.0%\n", " Outputs : probe ringdown monitor\n" ] } ], "source": [ "def print_simulation_summary(\n", " sim, stage_label, wavelength_nm, bandwidth_rel, output_note\n", "):\n", " \"\"\"Print a compact simulation summary without dumping the full object.\"\"\"\n", " grid = sim.grid.num_cells\n", " total_cells = int(np.prod(grid))\n", " print(f\"── {stage_label} ─────────────────────────────────────────────────\")\n", " print(\n", " f\" Domain : {sim.size[0]:.3f} × {sim.size[1]:.3f} × {sim.size[2]:.3f} µm\"\n", " )\n", " print(f\" Grid cells : {grid[0]} × {grid[1]} × {grid[2]} = {total_cells:,}\")\n", " print(f\" Run time : {sim.run_time * 1e12:.1f} ps\")\n", " print(\n", " f\" Excitation : Ey dipole @ {wavelength_nm:.1f} nm, Δf/f = {bandwidth_rel * 100:.1f}%\"\n", " )\n", " print(f\" Outputs : {output_note}\")\n", "\n", "\n", "sim_scout = GEOMETRY_SETUP.create_q_scout_simulation(\n", " run_time_ps=SCOUT_RUN_TIME_PS,\n", " min_steps_per_wvl=MIN_STEPS_PER_WVL,\n", ")\n", "\n", "print_simulation_summary(\n", " sim_scout,\n", " stage_label=\"Scout Simulation\",\n", " wavelength_nm=SCOUT_WAVELENGTH_UM * 1e3,\n", " bandwidth_rel=SCOUT_BANDWIDTH,\n", " output_note=\"probe ringdown monitor\",\n", ")" ] }, { "cell_type": "code", "execution_count": 8, "id": "15-scout-run", "metadata": { "execution": { "iopub.execute_input": "2026-04-07T18:23:05.388127Z", "iopub.status.busy": "2026-04-07T18:23:05.388050Z", "iopub.status.idle": "2026-04-07T18:23:05.422198Z", "shell.execute_reply": "2026-04-07T18:23:05.421876Z" } }, "outputs": [ { "data": { "text/html": [ "
14:18:46 -03 Created task 'diamond_cavity_scout_analysis_full' with resource_id \n",
       "             'fdve-3e612018-7d42-41bd-925f-806019414120' and task_type 'FDTD'.  \n",
       "
\n" ], "text/plain": [ "\u001b[2;36m14:18:46 -03\u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'diamond_cavity_scout_analysis_full'\u001b[0m with resource_id \n", "\u001b[2;36m \u001b[0m\u001b[32m'fdve-3e612018-7d42-41bd-925f-806019414120'\u001b[0m and task_type \u001b[32m'FDTD'\u001b[0m. \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
             View task using web UI at                                          \n",
       "             'https://tidy3d.simulation.cloud/workbench?taskId=fdve-3e612018-7d4\n",
       "             2-41bd-925f-806019414120'.                                         \n",
       "
\n" ], "text/plain": [ "\u001b[2;36m \u001b[0m\u001b[2;36m \u001b[0mView task using web UI at \n", "\u001b[2;36m \u001b[0m\u001b]8;id=599938;https://tidy3d.simulation.cloud/workbench?taskId=fdve-3e612018-7d42-41bd-925f-806019414120\u001b\\\u001b[32m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=146584;https://tidy3d.simulation.cloud/workbench?taskId=fdve-3e612018-7d42-41bd-925f-806019414120\u001b\\\u001b[32mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=599938;https://tidy3d.simulation.cloud/workbench?taskId=fdve-3e612018-7d42-41bd-925f-806019414120\u001b\\\u001b[32m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=8202;https://tidy3d.simulation.cloud/workbench?taskId=fdve-3e612018-7d42-41bd-925f-806019414120\u001b\\\u001b[32mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=599938;https://tidy3d.simulation.cloud/workbench?taskId=fdve-3e612018-7d42-41bd-925f-806019414120\u001b\\\u001b[32m-3e612018-7d4\u001b[0m\u001b]8;;\u001b\\\n", "\u001b[2;36m \u001b[0m\u001b]8;id=599938;https://tidy3d.simulation.cloud/workbench?taskId=fdve-3e612018-7d42-41bd-925f-806019414120\u001b\\\u001b[32m2-41bd-925f-806019414120'\u001b[0m\u001b]8;;\u001b\\. \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
             Task folder: 'default'.                                            \n",
       "
\n" ], "text/plain": [ "\u001b[2;36m \u001b[0m\u001b[2;36m \u001b[0mTask folder: \u001b]8;id=709261;https://tidy3d.simulation.cloud/folders/folder-431883bc-afbb-4cc8-a95f-ddfeaeeb059a\u001b\\\u001b[32m'default'\u001b[0m\u001b]8;;\u001b\\. \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "db2643d04aa545fa9a8f096eb23c988e", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Output()" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
\n",
       "
\n"
      ],
      "text/plain": [
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
14:18:49 -03 Estimated FlexCredit cost: 2.800. Minimum cost depends on task     \n",
       "             execution details. Use 'web.real_cost(task_id)' to get the billed  \n",
       "             FlexCredit cost after a simulation run.                            \n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:18:49 -03\u001b[0m\u001b[2;36m \u001b[0mEstimated FlexCredit cost: \u001b[1;36m2.800\u001b[0m. Minimum cost depends on task     \n",
       "\u001b[2;36m             \u001b[0mexecution details. Use \u001b[32m'web.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m to get the billed  \n",
       "\u001b[2;36m             \u001b[0mFlexCredit cost after a simulation run.                            \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
14:18:51 -03 status = queued                                                    \n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:18:51 -03\u001b[0m\u001b[2;36m \u001b[0mstatus = queued                                                    \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
             To cancel the simulation, use 'web.abort(task_id)' or              \n",
       "             'web.delete(task_id)' or abort/delete the task in the web UI.      \n",
       "             Terminating the Python script will not stop the job running on the \n",
       "             cloud.                                                             \n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mTo cancel the simulation, use \u001b[32m'web.abort\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m or              \n",
       "\u001b[2;36m             \u001b[0m\u001b[32m'web.delete\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m or abort/delete the task in the web UI.      \n",
       "\u001b[2;36m             \u001b[0mTerminating the Python script will not stop the job running on the \n",
       "\u001b[2;36m             \u001b[0mcloud.                                                             \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d274876b5a7049f2ab57b0c94169f16a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
14:19:01 -03 status = preprocess                                                \n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:19:01 -03\u001b[0m\u001b[2;36m \u001b[0mstatus = preprocess                                                \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
14:19:06 -03 starting up solver                                                 \n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:19:06 -03\u001b[0m\u001b[2;36m \u001b[0mstarting up solver                                                 \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
14:19:07 -03 running solver                                                     \n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:19:07 -03\u001b[0m\u001b[2;36m \u001b[0mrunning solver                                                     \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "28aa1377b2494ccc9427377022d3dad6",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
\n",
       "
\n"
      ],
      "text/plain": [
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
14:23:49 -03 status = postprocess                                               \n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:23:49 -03\u001b[0m\u001b[2;36m \u001b[0mstatus = postprocess                                               \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ce9c5e0591594f1683986532ee350496",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
14:23:50 -03 status = success                                                   \n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:23:50 -03\u001b[0m\u001b[2;36m \u001b[0mstatus = success                                                   \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
14:23:52 -03 View simulation result at                                          \n",
       "             'https://tidy3d.simulation.cloud/workbench?taskId=fdve-3e612018-7d4\n",
       "             2-41bd-925f-806019414120'.                                         \n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:23:52 -03\u001b[0m\u001b[2;36m \u001b[0mView simulation result at                                          \n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=970557;https://tidy3d.simulation.cloud/workbench?taskId=fdve-3e612018-7d42-41bd-925f-806019414120\u001b\\\u001b[4;34m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=667821;https://tidy3d.simulation.cloud/workbench?taskId=fdve-3e612018-7d42-41bd-925f-806019414120\u001b\\\u001b[4;34mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=970557;https://tidy3d.simulation.cloud/workbench?taskId=fdve-3e612018-7d42-41bd-925f-806019414120\u001b\\\u001b[4;34m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=87222;https://tidy3d.simulation.cloud/workbench?taskId=fdve-3e612018-7d42-41bd-925f-806019414120\u001b\\\u001b[4;34mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=970557;https://tidy3d.simulation.cloud/workbench?taskId=fdve-3e612018-7d42-41bd-925f-806019414120\u001b\\\u001b[4;34m-3e612018-7d4\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=970557;https://tidy3d.simulation.cloud/workbench?taskId=fdve-3e612018-7d42-41bd-925f-806019414120\u001b\\\u001b[4;34m2-41bd-925f-806019414120'\u001b[0m\u001b]8;;\u001b\\\u001b[4;34m.\u001b[0m                                         \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1bba587c35a84efbac7d4e1ed71867c6",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
\n",
       "
\n"
      ],
      "text/plain": [
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
14:23:56 -03 Loading simulation from                                            \n",
       "             /home/filipe/Desktop/GITHUB/EAPT-203/tidy3d-community-library/noteb\n",
       "             ooks/tidy3d_diamond_photonic_cavity_runtime/data/results/results_sc\n",
       "             out_analysis_full_0.136um.hdf5                                     \n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:23:56 -03\u001b[0m\u001b[2;36m \u001b[0mLoading simulation from                                            \n",
       "\u001b[2;36m             \u001b[0m\u001b[35m/home/filipe/Desktop/GITHUB/EAPT-203/tidy3d-community-library/noteb\u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[35mooks/tidy3d_diamond_photonic_cavity_runtime/data/results/\u001b[0m\u001b[95mresults_sc\u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[95mout_analysis_full_0.136um.hdf5\u001b[0m                                     \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
             WARNING: Simulation final field decay value of 0.229 is greater    \n",
       "             than the simulation shutoff threshold of 1e-05. Consider running   \n",
       "             the simulation again with a larger 'run_time' duration for more    \n",
       "             accurate results.                                                  \n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING: Simulation final field decay value of \u001b[0m\u001b[1;36m0.229\u001b[0m\u001b[31m is greater    \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31mthan the simulation shutoff threshold of \u001b[0m\u001b[1;36m1e-05\u001b[0m\u001b[31m. Consider running   \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31mthe simulation again with a larger \u001b[0m\u001b[32m'run_time'\u001b[0m\u001b[31m duration for more    \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31maccurate results.                                                  \u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scout simulation complete: results_scout_analysis_full_0.136um.hdf5\n"
     ]
    }
   ],
   "source": [
    "if RUN_SIMULATIONS:\n",
    "    import tidy3d.web as web\n",
    "\n",
    "    data_scout = web.run(\n",
    "        sim_scout,\n",
    "        task_name=SCOUT_TASK_NAME,\n",
    "        path=str(SCOUT_RUN_RESULTS),\n",
    "    )\n",
    "    print(f\"Scout simulation complete: {SCOUT_RUN_RESULTS.name}\")\n",
    "else:\n",
    "    print(f\"Loaded scout cache: {SCOUT_LOAD_RESULTS.name}\")\n",
    "    data_scout = td.SimulationData.from_file(str(SCOUT_LOAD_RESULTS))\n",
    "    print(\"  Probe time trace ready for resonance extraction.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16-qfactor-header",
   "metadata": {},
   "source": [
    "\n",
    "## 5. Extract the Cavity Q-Factor\n",
    "\n",
    "The Tidy3D **[`ResonanceFinder`](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.plugins.resonance.ResonanceFinder.html)** plugin decomposes the time-domain ringdown $E_y(t)$ into a sum of decaying sinusoids using a matrix-pencil (Prony) method:\n",
    "\n",
    "$$E_y(t) \\approx \\sum_k A_k \\, e^{-(\\omega_k / 2Q_k)\\,t} \\cos(\\omega_k t + \\phi_k)$$\n",
    "\n",
    "Each mode is characterized by its frequency $\\omega_k$ and quality factor $Q_k = \\omega_k \\tau_k / 2$, where $\\tau_k$ is the energy decay time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "17-qfactor-analysis",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-04-07T18:23:05.423803Z",
     "iopub.status.busy": "2026-04-07T18:23:05.423717Z",
     "iopub.status.idle": "2026-04-07T18:23:06.901305Z",
     "shell.execute_reply": "2026-04-07T18:23:06.901051Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-- Q-factor extraction --\n",
      "  Resonance wavelength : 639.305 nm\n",
      "  Quality factor  Q    : 67767\n",
      "  Energy decay time tau  : 46.00 ps\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "
"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def extract_resonance(\n",
    "    data_scout,\n",
    "    monitor_name=\"probe\",\n",
    "    wavelength_centre_um=None,\n",
    "    bandwidth_rel=None,\n",
    "    q_min=1e3,\n",
    "    q_max=1e6,\n",
    "    init_num_freqs=400,\n",
    "    rcond=1e-5,\n",
    "):\n",
    "    \"\"\"\n",
    "    Extract the dominant cavity resonance from the scout ringdown.\n",
    "\n",
    "    Uses the Tidy3D ResonanceFinder (matrix-pencil / Prony method) via\n",
    "    run_raw_signal() on the Ey time trace.  freq_window is computed\n",
    "    automatically from the broadband source parameters.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    data_scout : td.SimulationData\n",
    "    monitor_name : str\n",
    "    wavelength_centre_um : float  -- source centre wavelength [um]; defaults to SCOUT_WAVELENGTH_UM\n",
    "    bandwidth_rel : float  -- relative bandwidth; defaults to SCOUT_BANDWIDTH\n",
    "    q_min, q_max : float  -- Q-factor acceptance window\n",
    "    init_num_freqs, rcond : ResonanceFinder hyper-parameters\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    dict with keys: freq_Hz, wavelength_um, Q, decay_time_ps, amplitude\n",
    "    \"\"\"\n",
    "    if wavelength_centre_um is None:\n",
    "        wavelength_centre_um = SCOUT_WAVELENGTH_UM\n",
    "    if bandwidth_rel is None:\n",
    "        bandwidth_rel = SCOUT_BANDWIDTH\n",
    "\n",
    "    mon = data_scout[monitor_name]\n",
    "    ey = mon.Ey.values.squeeze().astype(complex)\n",
    "    t = mon.Ey.coords[\"t\"].values\n",
    "    dt = float(t[1] - t[0])  # time step [s]\n",
    "\n",
    "    # Frequency search window: source centre +/- 1 bandwidth\n",
    "    f0 = C0 / (wavelength_centre_um * 1e-6)\n",
    "    fwidth = f0 * bandwidth_rel\n",
    "    freq_window = (f0 - fwidth, f0 + fwidth)\n",
    "\n",
    "    rf = ResonanceFinder(\n",
    "        freq_window=freq_window,\n",
    "        init_num_freqs=init_num_freqs,\n",
    "        rcond=rcond,\n",
    "    )\n",
    "    # run_raw_signal takes a complex 1-D signal and the time step\n",
    "    res = rf.run_raw_signal(signal=ey, time_step=dt)\n",
    "    df = (\n",
    "        res.to_dataframe()\n",
    "    )  # index = freq [Hz]; cols: decay, Q, amplitude, phase, error\n",
    "\n",
    "    df = df[(df.index > 0) & (df[\"Q\"] > q_min) & (df[\"Q\"] < q_max)]\n",
    "    df = df.sort_values(\"amplitude\", ascending=False)\n",
    "\n",
    "    if df.empty:\n",
    "        tail_amp = float(np.abs(ey[-1]))\n",
    "        peak_amp = float(np.max(np.abs(ey)))\n",
    "        if peak_amp > 0 and tail_amp / peak_amp > 1e-3:\n",
    "            raise RuntimeError(\n",
    "                \"ResonanceFinder found no valid modes because the scout \"\n",
    "                \"signal has not decayed by the end of the run. \"\n",
    "                \"The loaded scout result is likely unstable or too short; \"\n",
    "                \"use a clean cache or rerun Stage 1 with a longer runtime.\"\n",
    "            )\n",
    "        raise RuntimeError(\n",
    "            \"ResonanceFinder found no valid modes. \"\n",
    "            \"Try adjusting q_min or the simulation bandwidth.\"\n",
    "        )\n",
    "\n",
    "    freq = float(df.index[0])\n",
    "    Q = float(df[\"Q\"].iloc[0])\n",
    "    tau_ps = Q / (np.pi * freq) * 1e12\n",
    "    wl_um = (C0 / freq) * 1e6\n",
    "\n",
    "    return {\n",
    "        \"freq_Hz\": freq,\n",
    "        \"wavelength_um\": wl_um,\n",
    "        \"Q\": Q,\n",
    "        \"decay_time_ps\": tau_ps,\n",
    "        \"amplitude\": float(df[\"amplitude\"].iloc[0]),\n",
    "    }\n",
    "\n",
    "\n",
    "print(\"-- Q-factor extraction --\")\n",
    "q_results = extract_resonance(data_scout)\n",
    "wavelength_lockin = q_results[\"wavelength_um\"]\n",
    "print(f\"  Resonance wavelength : {wavelength_lockin * 1e3:.3f} nm\")\n",
    "print(f\"  Quality factor  Q    : {q_results['Q']:.0f}\")\n",
    "print(f\"  Energy decay time tau  : {q_results['decay_time_ps']:.2f} ps\")\n",
    "\n",
    "# -- Ringdown visualisation\n",
    "probe = data_scout[\"probe\"]\n",
    "ey = probe.Ey.values.squeeze()\n",
    "t = probe.Ey.coords[\"t\"].values * 1e12\n",
    "env = np.abs(hilbert(ey))\n",
    "\n",
    "dt = np.diff(t[:2])[0] * 1e-12\n",
    "freqs_fft = np.fft.rfftfreq(len(ey), d=dt)\n",
    "spec = np.abs(np.fft.rfft(ey))\n",
    "wl_spec = np.where(freqs_fft[1:] > 0, C0 / freqs_fft[1:] * 1e9, np.nan)\n",
    "mask = (wl_spec > 550) & (wl_spec < 750)\n",
    "wl_plot = wl_spec[mask] if np.any(mask) else wl_spec\n",
    "spec_plot = spec[1:][mask] if np.any(mask) else spec[1:]\n",
    "\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(11, 3.8))\n",
    "\n",
    "signal_abs = np.abs(ey) / np.max(np.abs(ey))\n",
    "env_n = env / env.max()\n",
    "ringdown_floor = 1e-6\n",
    "\n",
    "ax1.semilogy(\n",
    "    t,\n",
    "    np.clip(signal_abs, ringdown_floor, None),\n",
    "    color=BLUE,\n",
    "    lw=0.9,\n",
    "    alpha=0.55,\n",
    "    label=\"$|E_y(t)|$\",\n",
    ")\n",
    "ax1.semilogy(\n",
    "    t, np.clip(env_n, ringdown_floor, None), color=RED, lw=1.8, label=\"Envelope |E|\"\n",
    ")\n",
    "ax1.set_xlabel(\"Time (ps)\")\n",
    "ax1.set_ylabel(\"Normalised amplitude (log scale)\")\n",
    "ax1.set_title(\"Time-Domain Ringdown\")\n",
    "ax1.set_ylim(ringdown_floor, 1.2)\n",
    "ax1.grid(True, which=\"both\", alpha=0.25)\n",
    "ax1.legend()\n",
    "\n",
    "ax2.plot(wl_plot, spec_plot / spec_plot.max(), color=RED, lw=1.8)\n",
    "ax2.axvline(\n",
    "    wavelength_lockin * 1e3,\n",
    "    color=BLUE,\n",
    "    ls=\"--\",\n",
    "    lw=1.8,\n",
    "    label=f\"$\\\\lambda_0$ = {wavelength_lockin * 1e3:.2f} nm\\nQ = {q_results['Q']:.0f}\",\n",
    ")\n",
    "ax2.set_xlabel(\"Wavelength (nm)\")\n",
    "ax2.set_ylabel(\"Normalised PSD\")\n",
    "ax2.set_title(\"Resonance Spectrum\")\n",
    "ax2.legend()\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18-lockin-header",
   "metadata": {},
   "source": [
    "\n",
    "## 6. Run a Narrowband Characterization Simulation\n",
    "\n",
    "The lock-in stage uses the resonance wavelength detected in Stage 1 as its centre frequency with a narrow 2 % bandwidth. This minimizes spectral leakage and maximizes the steady-state field amplitude at the resonance. The monitor suite below follows the same configuration used in the shared simulation scripts and includes seven monitors:\n",
    "\n",
    "| Monitor | Type | Purpose |\n",
    "|---------|------|----------|\n",
    "| `probe` | [`FieldTimeMonitor`](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.FieldTimeMonitor.html) | Q verification from ringdown |\n",
    "| `flux` | [`FluxMonitor`](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.FluxMonitor.html) | In-plane flux check at the cavity plane |\n",
    "| `field_near` | [`FieldMonitor`](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.FieldMonitor.html) (2-D, z = 0) | Near-field mode profile |\n",
    "| `farfield_cartesian` | [`FieldProjectionCartesianMonitor`](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.FieldProjectionCartesianMonitor.html) | Cartesian projected far field |\n",
    "| `farfield_kspace` | [`FieldProjectionKSpaceMonitor`](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.FieldProjectionKSpaceMonitor.html) | k-space back-focal-plane map |\n",
    "| `farfield_angles` | [`FieldProjectionAngleMonitor`](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.FieldProjectionAngleMonitor.html) | Angular radiation pattern |\n",
    "| `fld_3d_box` | [`FieldMonitor`](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.FieldMonitor.html) (3-D volume) | Mode volume integration |"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "19-lockin-setup",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-04-07T18:23:06.902605Z",
     "iopub.status.busy": "2026-04-07T18:23:06.902534Z",
     "iopub.status.idle": "2026-04-07T18:23:06.929289Z",
     "shell.execute_reply": "2026-04-07T18:23:06.929079Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "======================================================================\n",
      "SETTING UP GEOMETRY PARAMETERS\n",
      "======================================================================\n",
      "✓ Geometry parameters loaded\n",
      "  - Cavity thickness: 0.136 µm\n",
      "  - Wavelength: 0.6393048883783318 µm\n",
      "  - Optimized padding: x=[-5.0, -5.0], y=[2.0, 2.0], z=[2.0, 2.0]\n",
      "\n",
      "Creating diamond medium with full Sellmeier dispersion...\n",
      "  - Using full Sellmeier dispersion model\n",
      "  - Coefficients: B1=0.3306, C1=0.175²; B2=4.3356, C2=0.106²\n",
      "  - Reference n(λ=0.639 µm) = 2.411543\n",
      "  - Full frequency-dependent dispersion enabled for FDTD simulation\n",
      "\n",
      "Creating complete simulation...\n",
      "\n",
      "Generating local geometry from embedded cavity and hole specifications...\n",
      "  - Using cell: TOP\n",
      "  - Original bbox: x[-15.675, 15.675], y[-0.157, 0.157]\n",
      "  - Geometry bbox: x[-15.675, 15.675], y[-0.157, 0.157]\n",
      "  - Simulation domain: left=-10.675, right=10.675, bottom=-2.157, top=2.157\n",
      "  - X truncation: 31.350 → 21.350 µm (reduced by 10.000 µm)\n",
      "  - Simulation size: (21.350, 4.314, 4.136) µm\n",
      "  - Simulation center: (0.000, 0.000, 0.000) µm\n",
      "\n",
      "Creating core structure...\n",
      "  - Trapezoidal core (native sidewall): sidewall_angle=15.60°\n",
      "  - Bottom width (x × y): 31.350 µm × 0.314 µm\n",
      "  - Top width    (x × y): 31.274 µm × 0.238 µm\n",
      "  - Thickness: 0.136 µm\n",
      "\n",
      "Creating hole structures...\n",
      "  - Using holes cell: TOP\n",
      "  - Imported 100 hole polygon(s)\n",
      "  - Created 1 hole structure(s)\n",
      "  - Thickness: 0.136 µm\n",
      "\n",
      "Creating sources and monitors...\n",
      "  - Source: Point dipole at (0.000, 0.000, 0.0)\n",
      "  - Frequency: 468.935 THz\n",
      "  - Bandwidth: 9.38 THz\n",
      "  - Created 3 basic monitors\n",
      "\n",
      "Creating improved far-field monitors...\n",
      "  - Positioning monitors at z = 1.5 µm\n",
      "  - Monitor size: 8.0 µm\n",
      "  - Created 3 improved far-field monitors\n",
      "✓ Complete simulation created successfully\n",
      "  - Structures: 2\n",
      "  - Monitors: 7\n",
      "  - Run time: 8.0 ps\n",
      "  - Grid: min_steps_per_wvl=18 with wavelength=0.6393048883783318 µm\n",
      "  - Boundaries: PML(8 layers)\n",
      "── Lock-in Simulation ─────────────────────────────────────────────────\n",
      "  Domain     : 21.350 × 4.314 × 4.136 µm\n",
      "  Grid cells : 1475 × 162 × 150 = 35,842,500\n",
      "  Run time   : 8.0 ps\n",
      "  Excitation : Ey dipole @ 639.3 nm, Δf/f = 2.0%\n",
      "  Outputs    : probe, flux, near field, 3-D box, cartesian and angular far-field projections\n"
     ]
    }
   ],
   "source": [
    "LOCKIN_SETUP = NotebookSimulationSetup(\n",
    "    thickness_um=THICKNESS_UM,\n",
    "    wavelength_um=wavelength_lockin,\n",
    "    source_bandwidth_rel=LOCKIN_BANDWIDTH,\n",
    "    cavity_gds=CAVITY_GDS,\n",
    "    holes_gds=HOLES_GDS,\n",
    "    sidewall_angle_deg=SIDEWALL_ANGLE_DEG,\n",
    ")\n",
    "\n",
    "sim_lockin = LOCKIN_SETUP.create_simulation(\n",
    "    run_time_ps=LOCKIN_RUN_TIME_PS,\n",
    "    min_steps_per_wvl=MIN_STEPS_PER_WVL,\n",
    ")\n",
    "\n",
    "print_simulation_summary(\n",
    "    sim_lockin,\n",
    "    stage_label=\"Lock-in Simulation\",\n",
    "    wavelength_nm=wavelength_lockin * 1e3,\n",
    "    bandwidth_rel=LOCKIN_BANDWIDTH,\n",
    "    output_note=\"probe, flux, near field, 3-D box, cartesian and angular far-field projections\",\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "20-lockin-run",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-04-07T18:23:06.930445Z",
     "iopub.status.busy": "2026-04-07T18:23:06.930385Z",
     "iopub.status.idle": "2026-04-07T18:23:07.007587Z",
     "shell.execute_reply": "2026-04-07T18:23:07.007250Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "
14:24:04 -03 Created task 'diamond_cavity_lockin_analysis_full' with resource_id\n",
       "             'fdve-b6fb70bf-ca4c-4372-8174-3aad47239c89' and task_type 'FDTD'.  \n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:24:04 -03\u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'diamond_cavity_lockin_analysis_full'\u001b[0m with resource_id\n",
       "\u001b[2;36m             \u001b[0m\u001b[32m'fdve-b6fb70bf-ca4c-4372-8174-3aad47239c89'\u001b[0m and task_type \u001b[32m'FDTD'\u001b[0m.  \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
             View task using web UI at                                          \n",
       "             'https://tidy3d.simulation.cloud/workbench?taskId=fdve-b6fb70bf-ca4\n",
       "             c-4372-8174-3aad47239c89'.                                         \n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mView task using web UI at                                          \n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=870728;https://tidy3d.simulation.cloud/workbench?taskId=fdve-b6fb70bf-ca4c-4372-8174-3aad47239c89\u001b\\\u001b[32m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=921318;https://tidy3d.simulation.cloud/workbench?taskId=fdve-b6fb70bf-ca4c-4372-8174-3aad47239c89\u001b\\\u001b[32mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=870728;https://tidy3d.simulation.cloud/workbench?taskId=fdve-b6fb70bf-ca4c-4372-8174-3aad47239c89\u001b\\\u001b[32m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=822194;https://tidy3d.simulation.cloud/workbench?taskId=fdve-b6fb70bf-ca4c-4372-8174-3aad47239c89\u001b\\\u001b[32mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=870728;https://tidy3d.simulation.cloud/workbench?taskId=fdve-b6fb70bf-ca4c-4372-8174-3aad47239c89\u001b\\\u001b[32m-b6fb70bf-ca4\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=870728;https://tidy3d.simulation.cloud/workbench?taskId=fdve-b6fb70bf-ca4c-4372-8174-3aad47239c89\u001b\\\u001b[32mc-4372-8174-3aad47239c89'\u001b[0m\u001b]8;;\u001b\\.                                         \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
             Task folder: 'default'.                                            \n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mTask folder: \u001b]8;id=121380;https://tidy3d.simulation.cloud/folders/folder-431883bc-afbb-4cc8-a95f-ddfeaeeb059a\u001b\\\u001b[32m'default'\u001b[0m\u001b]8;;\u001b\\.                                            \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2f32071d16ea4469a92923faeb4b2a10",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
\n",
       "
\n"
      ],
      "text/plain": [
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
14:24:07 -03 Estimated FlexCredit cost: 3.561. Minimum cost depends on task     \n",
       "             execution details. Use 'web.real_cost(task_id)' to get the billed  \n",
       "             FlexCredit cost after a simulation run.                            \n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:24:07 -03\u001b[0m\u001b[2;36m \u001b[0mEstimated FlexCredit cost: \u001b[1;36m3.561\u001b[0m. Minimum cost depends on task     \n",
       "\u001b[2;36m             \u001b[0mexecution details. Use \u001b[32m'web.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m to get the billed  \n",
       "\u001b[2;36m             \u001b[0mFlexCredit cost after a simulation run.                            \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
14:24:09 -03 status = queued                                                    \n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:24:09 -03\u001b[0m\u001b[2;36m \u001b[0mstatus = queued                                                    \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
             To cancel the simulation, use 'web.abort(task_id)' or              \n",
       "             'web.delete(task_id)' or abort/delete the task in the web UI.      \n",
       "             Terminating the Python script will not stop the job running on the \n",
       "             cloud.                                                             \n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mTo cancel the simulation, use \u001b[32m'web.abort\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m or              \n",
       "\u001b[2;36m             \u001b[0m\u001b[32m'web.delete\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m or abort/delete the task in the web UI.      \n",
       "\u001b[2;36m             \u001b[0mTerminating the Python script will not stop the job running on the \n",
       "\u001b[2;36m             \u001b[0mcloud.                                                             \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "002d2c509fc445ae8994675b42682a22",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
14:24:19 -03 starting up solver                                                 \n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:24:19 -03\u001b[0m\u001b[2;36m \u001b[0mstarting up solver                                                 \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
             running solver                                                     \n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mrunning solver                                                     \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1e9aff7b347c4ac5868f77445d8f6e91",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
\n",
       "
\n"
      ],
      "text/plain": [
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
14:29:00 -03 status = postprocess                                               \n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:29:00 -03\u001b[0m\u001b[2;36m \u001b[0mstatus = postprocess                                               \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b70d1d74f88d407589c3dd957c075dac",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
14:29:04 -03 status = success                                                   \n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:29:04 -03\u001b[0m\u001b[2;36m \u001b[0mstatus = success                                                   \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
14:29:06 -03 View simulation result at                                          \n",
       "             'https://tidy3d.simulation.cloud/workbench?taskId=fdve-b6fb70bf-ca4\n",
       "             c-4372-8174-3aad47239c89'.                                         \n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:29:06 -03\u001b[0m\u001b[2;36m \u001b[0mView simulation result at                                          \n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=567293;https://tidy3d.simulation.cloud/workbench?taskId=fdve-b6fb70bf-ca4c-4372-8174-3aad47239c89\u001b\\\u001b[4;34m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=799645;https://tidy3d.simulation.cloud/workbench?taskId=fdve-b6fb70bf-ca4c-4372-8174-3aad47239c89\u001b\\\u001b[4;34mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=567293;https://tidy3d.simulation.cloud/workbench?taskId=fdve-b6fb70bf-ca4c-4372-8174-3aad47239c89\u001b\\\u001b[4;34m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=234414;https://tidy3d.simulation.cloud/workbench?taskId=fdve-b6fb70bf-ca4c-4372-8174-3aad47239c89\u001b\\\u001b[4;34mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=567293;https://tidy3d.simulation.cloud/workbench?taskId=fdve-b6fb70bf-ca4c-4372-8174-3aad47239c89\u001b\\\u001b[4;34m-b6fb70bf-ca4\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=567293;https://tidy3d.simulation.cloud/workbench?taskId=fdve-b6fb70bf-ca4c-4372-8174-3aad47239c89\u001b\\\u001b[4;34mc-4372-8174-3aad47239c89'\u001b[0m\u001b]8;;\u001b\\\u001b[4;34m.\u001b[0m                                         \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5db0f19aabd941eea4bedc014dbce35f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
\n",
       "
\n"
      ],
      "text/plain": [
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
14:29:17 -03 Loading simulation from                                            \n",
       "             /home/filipe/Desktop/GITHUB/EAPT-203/tidy3d-community-library/noteb\n",
       "             ooks/tidy3d_diamond_photonic_cavity_runtime/data/results/results_lo\n",
       "             ckin_analysis_full_0.136um.hdf5                                    \n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:29:17 -03\u001b[0m\u001b[2;36m \u001b[0mLoading simulation from                                            \n",
       "\u001b[2;36m             \u001b[0m\u001b[35m/home/filipe/Desktop/GITHUB/EAPT-203/tidy3d-community-library/noteb\u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[35mooks/tidy3d_diamond_photonic_cavity_runtime/data/results/\u001b[0m\u001b[95mresults_lo\u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[95mckin_analysis_full_0.136um.hdf5\u001b[0m                                    \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
             WARNING: Simulation final field decay value of 0.341 is greater    \n",
       "             than the simulation shutoff threshold of 1e-05. Consider running   \n",
       "             the simulation again with a larger 'run_time' duration for more    \n",
       "             accurate results.                                                  \n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING: Simulation final field decay value of \u001b[0m\u001b[1;36m0.341\u001b[0m\u001b[31m is greater    \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31mthan the simulation shutoff threshold of \u001b[0m\u001b[1;36m1e-05\u001b[0m\u001b[31m. Consider running   \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31mthe simulation again with a larger \u001b[0m\u001b[32m'run_time'\u001b[0m\u001b[31m duration for more    \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31maccurate results.                                                  \u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lock-in simulation complete: results_lockin_analysis_full_0.136um.hdf5\n"
     ]
    }
   ],
   "source": [
    "if RUN_SIMULATIONS and RUN_LOCKIN_STAGE:\n",
    "    import tidy3d.web as web\n",
    "\n",
    "    data = web.run(\n",
    "        sim_lockin,\n",
    "        task_name=LOCKIN_TASK_NAME,\n",
    "        path=str(LOCKIN_RUN_RESULTS),\n",
    "    )\n",
    "    print(f\"Lock-in simulation complete: {LOCKIN_RUN_RESULTS.name}\")\n",
    "else:\n",
    "    if RUN_SIMULATIONS and not RUN_LOCKIN_STAGE:\n",
    "        print(\n",
    "            f\"{SIMULATION_PROFILE} selected: skipping Stage 2 rerun and \"\n",
    "            f\"reusing {LOCKIN_LOAD_RESULTS.name}.\"\n",
    "        )\n",
    "    else:\n",
    "        print(f\"Loaded lock-in cache: {LOCKIN_LOAD_RESULTS.name}\")\n",
    "    data = td.SimulationData.from_file(str(LOCKIN_LOAD_RESULTS))\n",
    "    print(f\"  Monitor suite ready: {len(data.monitor_data)} datasets loaded.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21-nearfield-header",
   "metadata": {},
   "source": [
    "\n",
    "## 7. Near-Field Mode Analysis\n",
    "\n",
    "The `field_near` monitor records the electric field components $E_x, E_y, E_z$ on a 2-D cross-section at $z = 0$ (mid-plane of the slab). We visualize the total intensity $|\\mathbf{E}|^2 = |E_x|^2 + |E_y|^2 + |E_z|^2$, overlay the notebook-generated cavity outline, and characterize the lateral confinement by fitting 1-D Gaussian profiles.\n",
    "\n",
    "The **effective mode area** is defined as:\n",
    "\n",
    "$$A_{\\text{eff}} = \\frac{\\left(\\iint I \\, dA\\right)^2}{\\iint I^2 \\, dA}$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "22-nearfield-analysis",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-04-07T18:23:07.009183Z",
     "iopub.status.busy": "2026-04-07T18:23:07.009111Z",
     "iopub.status.idle": "2026-04-07T18:23:07.056414Z",
     "shell.execute_reply": "2026-04-07T18:23:07.056195Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "── Near-field analysis ──────────────────────────────────────────────────\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "
"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  1/e² width  w_x   = 1568 nm  (FWHM = 3691 nm)\n",
      "  1/e² width  w_y   = 197 nm  (FWHM = 464 nm)\n",
      "  Eff. mode area    = 1.6626 µm²  = 4.068 λ²\n"
     ]
    }
   ],
   "source": [
    "def _coords_to_um(arr):\n",
    "    \"\"\"Auto-convert coordinate array from metres to µm if needed.\"\"\"\n",
    "    return arr * 1e6 if np.max(np.abs(arr)) < 1e-3 else arr\n",
    "\n",
    "\n",
    "def extract_nearfield_intensity(data, monitor_name=\"field_near\"):\n",
    "    \"\"\"Return 2-D intensity map and coordinate arrays (in µm).\"\"\"\n",
    "    mon = data[monitor_name]\n",
    "    Ex = mon.Ex.isel(f=0).values.squeeze()\n",
    "    Ey = mon.Ey.isel(f=0).values.squeeze()\n",
    "    Ez = mon.Ez.isel(f=0).values.squeeze() if \"Ez\" in mon else 0.0\n",
    "    I = np.abs(Ex) ** 2 + np.abs(Ey) ** 2 + np.abs(Ez) ** 2\n",
    "    x = _coords_to_um(mon.Ey.coords[\"x\"].values)\n",
    "    y = _coords_to_um(mon.Ey.coords[\"y\"].values)\n",
    "    # Ensure I has shape (ny, nx) to match pcolormesh convention\n",
    "    if I.shape == (len(x), len(y)):\n",
    "        I = I.T\n",
    "    return I, x, y\n",
    "\n",
    "\n",
    "def crop_field(I, x, y, x_lim=(-2.0, 2.0), y_lim=(-0.7, 0.7)):\n",
    "    \"\"\"Crop a 2-D intensity map to the specified window (µm).\"\"\"\n",
    "    xi = (x >= x_lim[0]) & (x <= x_lim[1])\n",
    "    yi = (y >= y_lim[0]) & (y <= y_lim[1])\n",
    "    return I[np.ix_(yi, xi)], x[xi], y[yi]\n",
    "\n",
    "\n",
    "def overlay_gds_outline(ax, x_window, y_window, holes_gds=HOLES_GDS):\n",
    "    \"\"\"Overlay the notebook-generated cavity outline on top of the near-field map.\"\"\"\n",
    "    rect_kw_outer = dict(fill=False, ec=BLACK, lw=1.4, alpha=0.55, zorder=4)\n",
    "    rect_kw_inner = dict(fill=False, ec=WHITE, lw=0.8, alpha=0.95, zorder=5)\n",
    "    ax.add_patch(plt.Rectangle((X0, Y0), X1 - X0, Y1 - Y0, **rect_kw_outer))\n",
    "    ax.add_patch(plt.Rectangle((X0, Y0), X1 - X0, Y1 - Y0, **rect_kw_inner))\n",
    "\n",
    "    lib = gdstk.read_gds(str(holes_gds))\n",
    "    scale = lib.unit / 1e-6\n",
    "    cell = lib.top_level()[0]\n",
    "\n",
    "    for poly in cell.polygons:\n",
    "        if poly.layer != 0 or poly.datatype != 0:\n",
    "            continue\n",
    "        pts = poly.points * scale\n",
    "        if (\n",
    "            pts[:, 0].max() < x_window[0]\n",
    "            or pts[:, 0].min() > x_window[1]\n",
    "            or pts[:, 1].max() < y_window[0]\n",
    "            or pts[:, 1].min() > y_window[1]\n",
    "        ):\n",
    "            continue\n",
    "        ax.add_patch(\n",
    "            plt.Polygon(\n",
    "                pts,\n",
    "                closed=True,\n",
    "                fill=False,\n",
    "                ec=BLACK,\n",
    "                lw=1.0,\n",
    "                alpha=0.5,\n",
    "                zorder=4,\n",
    "                joinstyle=\"round\",\n",
    "            )\n",
    "        )\n",
    "        ax.add_patch(\n",
    "            plt.Polygon(\n",
    "                pts,\n",
    "                closed=True,\n",
    "                fill=False,\n",
    "                ec=WHITE,\n",
    "                lw=0.4,\n",
    "                alpha=0.95,\n",
    "                zorder=5,\n",
    "                joinstyle=\"round\",\n",
    "            )\n",
    "        )\n",
    "\n",
    "\n",
    "def plot_nearfield(I, x, y, wavelength_nm, cmap=None):\n",
    "    \"\"\"Display the near-field intensity map.\"\"\"\n",
    "    cmap = cmap or mono_cmap\n",
    "    I_n = I / I.max()\n",
    "    vmin, vmax = np.percentile(I_n, [0.1, 99.9])\n",
    "\n",
    "    fig, ax = plt.subplots(figsize=(8.2, 3.6))\n",
    "    im = ax.pcolormesh(x, y, I_n, cmap=cmap, vmin=vmin, vmax=vmax, shading=\"auto\")\n",
    "    overlay_gds_outline(ax, (x.min(), x.max()), (y.min(), y.max()))\n",
    "    cbar = plt.colorbar(im, ax=ax, fraction=0.025, pad=0.02)\n",
    "    cbar.set_label(\"$|\\\\mathbf{E}|^2$ (normalised)\")\n",
    "    ax.set_xlim(x.min(), x.max())\n",
    "    ax.set_ylim(y.min(), y.max())\n",
    "    ax.set_xlabel(\"x (µm)\")\n",
    "    ax.set_ylabel(\"y (µm)\")\n",
    "    ax.set_title(f\"Near-Field Intensity  –  $\\\\lambda_0$ = {wavelength_nm:.2f} nm\")\n",
    "    ax.set_aspect(\"equal\")\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "def gaussian_1d(x, A, x0, sigma, bg):\n",
    "    return A * np.exp(-2 * (x - x0) ** 2 / sigma**2) + bg\n",
    "\n",
    "\n",
    "def compute_confinement(I, x, y, wavelength_um):\n",
    "    \"\"\"\n",
    "    Quantify the lateral confinement from 1-D Gaussian fits.\n",
    "\n",
    "    Returns a dict with 1/e² widths, FWHM, effective area, and\n",
    "    effective area normalised to λ².\n",
    "    \"\"\"\n",
    "    results = {}\n",
    "    for axis_label, coords, profile in [\n",
    "        (\"x\", x, I.sum(axis=0)),\n",
    "        (\"y\", y, I.sum(axis=1)),\n",
    "    ]:\n",
    "        pn = profile / profile.max()\n",
    "        try:\n",
    "            p0 = [1.0, coords[np.argmax(pn)], 0.25, 0.0]\n",
    "            popt, _ = curve_fit(gaussian_1d, coords, pn, p0=p0, maxfev=8000)\n",
    "            w = abs(popt[2])  # 1/e² half-width\n",
    "        except RuntimeError:\n",
    "            above = coords[pn > np.exp(-2)]\n",
    "            w = (above[-1] - above[0]) / 2 if len(above) > 1 else np.nan\n",
    "        results[f\"w_{axis_label}_um\"] = w\n",
    "        results[f\"fwhm_{axis_label}_nm\"] = 2 * np.sqrt(2 * np.log(2)) * w * 1e3\n",
    "\n",
    "    dx = abs(np.diff(x[:2])[0])\n",
    "    dy = abs(np.diff(y[:2])[0])\n",
    "    A_eff = float(np.sum(I) ** 2 / np.sum(I**2) * dx * dy)\n",
    "    results[\"A_eff_um2\"] = A_eff\n",
    "    results[\"A_eff_lambda2\"] = A_eff / wavelength_um**2\n",
    "    return results\n",
    "\n",
    "\n",
    "# ── Run analysis ──────────────────────────────────────────────────────────────\n",
    "print(\"── Near-field analysis ──────────────────────────────────────────────────\")\n",
    "\n",
    "I_raw, x_raw, y_raw = extract_nearfield_intensity(data)\n",
    "I_nf, x_nf, y_nf = crop_field(I_raw, x_raw, y_raw)\n",
    "plot_nearfield(I_nf, x_nf, y_nf, wavelength_lockin * 1e3)\n",
    "\n",
    "conf = compute_confinement(I_nf, x_nf, y_nf, wavelength_lockin)\n",
    "print(\n",
    "    f\"  1/e² width  w_x   = {conf['w_x_um'] * 1e3:.0f} nm\"\n",
    "    f\"  (FWHM = {conf['fwhm_x_nm']:.0f} nm)\"\n",
    ")\n",
    "print(\n",
    "    f\"  1/e² width  w_y   = {conf['w_y_um'] * 1e3:.0f} nm\"\n",
    "    f\"  (FWHM = {conf['fwhm_y_nm']:.0f} nm)\"\n",
    ")\n",
    "print(\n",
    "    f\"  Eff. mode area    = {conf['A_eff_um2']:.4f} µm²\"\n",
    "    f\"  = {conf['A_eff_lambda2']:.3f} λ²\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23-modevolume-header",
   "metadata": {},
   "source": [
    "\n",
    "## 8. Mode Volume and Purcell Enhancement\n",
    "\n",
    "The **effective mode volume** is computed from the 3-D field distribution stored in `fld_3d_box`:\n",
    "\n",
    "$$V_{\\text{eff}} = \\frac{\\int \\varepsilon(\\mathbf{r})\\,|\\mathbf{E}(\\mathbf{r})|^2 \\, d^3r}{\\max\\!\\left[\\varepsilon(\\mathbf{r})\\,|\\mathbf{E}(\\mathbf{r})|^2\\right]}$$\n",
    "\n",
    "The permittivity $\\varepsilon(\\mathbf{r})$ is taken as $n_{\\text{diamond}}^2$ inside the slab and 1 (air) outside.  The Purcell factor follows directly from $Q$ and $V_{\\text{eff}}$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "24-modevolume-analysis",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-04-07T18:23:07.057609Z",
     "iopub.status.busy": "2026-04-07T18:23:07.057539Z",
     "iopub.status.idle": "2026-04-07T18:23:07.094855Z",
     "shell.execute_reply": "2026-04-07T18:23:07.094630Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "── Mode volume and Purcell factor ───────────────────────────────────────\n",
      "  Effective mode volume  V_eff  = 0.1204 µm³\n",
      "                                = 51.720 × (λ/2n)³\n",
      "  Quality factor         Q      = 67767\n",
      "  Purcell factor         F_P    = 796.6\n"
     ]
    }
   ],
   "source": [
    "def compute_mode_volume(\n",
    "    data, monitor_name=\"fld_3d_box\", wavelength_um=0.62, thickness_um=0.136\n",
    "):\n",
    "    \"\"\"\n",
    "    Compute the effective mode volume V_eff and its normalisation (λ/2n)³.\n",
    "\n",
    "    The permittivity is estimated analytically: n_diamond² inside the slab\n",
    "    (|z| < thickness_um/2) and 1 elsewhere.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    data : td.SimulationData\n",
    "    monitor_name : str\n",
    "    wavelength_um : float  — resonance wavelength [µm]\n",
    "    thickness_um : float   — slab thickness [µm]\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    V_eff_um3 : float  — effective mode volume [µm³]\n",
    "    V_norm    : float  — V_eff / (λ/2n)³  (dimensionless)\n",
    "    \"\"\"\n",
    "    mon = data[monitor_name]\n",
    "    Ex = mon.Ex.isel(f=0).values.squeeze()\n",
    "    Ey = mon.Ey.isel(f=0).values.squeeze()\n",
    "    Ez = mon.Ez.isel(f=0).values.squeeze() if \"Ez\" in mon else np.zeros_like(Ex)\n",
    "\n",
    "    x = _coords_to_um(mon.Ex.coords[\"x\"].values)\n",
    "    y = _coords_to_um(mon.Ex.coords[\"y\"].values)\n",
    "    z = _coords_to_um(mon.Ex.coords[\"z\"].values)\n",
    "\n",
    "    # Ensure field shape (nz, ny, nx)\n",
    "    I = np.abs(Ex) ** 2 + np.abs(Ey) ** 2 + np.abs(Ez) ** 2\n",
    "    if I.shape == (len(x), len(y), len(z)):\n",
    "        I = np.transpose(I, (2, 1, 0))\n",
    "\n",
    "    # Permittivity map\n",
    "    n = float(n_diamond(wavelength_um))\n",
    "    Z3d = z[:, None, None] * np.ones_like(I)\n",
    "    eps = np.where(np.abs(Z3d) < thickness_um / 2, n**2, 1.0)\n",
    "\n",
    "    eps_I = eps * I\n",
    "    dV = abs(np.diff(x[:2])[0]) * abs(np.diff(y[:2])[0]) * abs(np.diff(z[:2])[0])\n",
    "    V_eff = float(np.sum(eps_I) * dV / np.max(eps_I))\n",
    "\n",
    "    lambda_n = wavelength_um / n\n",
    "    V_norm = V_eff / (lambda_n / 2) ** 3\n",
    "    return V_eff, V_norm\n",
    "\n",
    "\n",
    "def compute_purcell(Q, V_eff_um3, wavelength_um):\n",
    "    \"\"\"\n",
    "    Purcell enhancement factor  F_P = (3/4π²) (λ/n)³ Q / V_eff.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    Q : float  — quality factor\n",
    "    V_eff_um3 : float  — effective mode volume [µm³]\n",
    "    wavelength_um : float  — resonance wavelength [µm]\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    float  — Purcell factor\n",
    "    \"\"\"\n",
    "    n = float(n_diamond(wavelength_um))\n",
    "    lam_n3 = (wavelength_um / n) ** 3  # (λ/n)³ [µm³]\n",
    "    return float((3.0 / (4.0 * np.pi**2)) * lam_n3 / V_eff_um3 * Q)\n",
    "\n",
    "\n",
    "print(\"── Mode volume and Purcell factor ───────────────────────────────────────\")\n",
    "V_eff, V_norm = compute_mode_volume(\n",
    "    data, wavelength_um=wavelength_lockin, thickness_um=THICKNESS_UM\n",
    ")\n",
    "Q_val = q_results[\"Q\"]\n",
    "F_P = compute_purcell(Q_val, V_eff, wavelength_lockin)\n",
    "\n",
    "print(f\"  Effective mode volume  V_eff  = {V_eff:.4f} µm³\")\n",
    "print(f\"                                = {V_norm:.3f} × (λ/2n)³\")\n",
    "print(f\"  Quality factor         Q      = {Q_val:.0f}\")\n",
    "print(f\"  Purcell factor         F_P    = {F_P:.1f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25-farfield-header",
   "metadata": {},
   "source": [
    "\n",
    "## 9. Far-Field Radiation Pattern and Collection Efficiency\n",
    "\n",
    "The [`FieldProjectionKSpaceMonitor`](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.FieldProjectionKSpaceMonitor.html) projects the fields onto a plane at height $z_{\\text{ff}}$ and records the complex amplitudes as a function of normalized wavevectors $(u_x, u_y) = (k_x/k_0, k_y/k_0)$. A circle of radius NA in this plane corresponds to the acceptance cone of an objective lens.\n",
    "\n",
    "In this notebook, collection efficiency is reported relative to the **total emission into both hemispheres**. Since the monitor samples one projected hemisphere, the monitored hemispheric power is multiplied by 2 in the denominator:\n",
    "\n",
    "$$\\eta_{\\text{NA}} = \\frac{\\displaystyle\\iint_{u_x^2 + u_y^2 \\leq \\text{NA}^2} I(u_x, u_y)\\, du_x\\, du_y}{\\displaystyle 2\\iint_{u_x^2 + u_y^2 \\leq 1} I(u_x, u_y)\\, du_x\\, du_y}$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "26-farfield-analysis",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-04-07T18:23:07.096194Z",
     "iopub.status.busy": "2026-04-07T18:23:07.096126Z",
     "iopub.status.idle": "2026-04-07T18:23:07.131575Z",
     "shell.execute_reply": "2026-04-07T18:23:07.131348Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "── Far-field radiation pattern ──────────────────────────────────────────\n",
      "  Collection efficiency at NA = 0.65: 31.2%\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "
"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def extract_kspace_intensity(data, monitor_name=\"farfield_kspace\"):\n",
    "    \"\"\"Return far-field intensity on the (ux, uy) k-space grid.\"\"\"\n",
    "    mon = data[monitor_name]\n",
    "    # Get field projections – try Etheta/Ephi first, fall back to Ex/Ey\n",
    "    if hasattr(mon, \"Etheta\"):\n",
    "        dims = mon.Etheta.dims\n",
    "        if \"r\" in dims:\n",
    "            Et = mon.Etheta.isel(f=0, r=0).values.squeeze()\n",
    "            Ep = mon.Ephi.isel(f=0, r=0).values.squeeze()\n",
    "        else:\n",
    "            Et = mon.Etheta.isel(f=0).values.squeeze()\n",
    "            Ep = mon.Ephi.isel(f=0).values.squeeze()\n",
    "    else:\n",
    "        Et = mon.Ex.isel(f=0).values.squeeze()\n",
    "        Ep = mon.Ey.isel(f=0).values.squeeze()\n",
    "    I = np.abs(Et) ** 2 + np.abs(Ep) ** 2\n",
    "    ux = np.asarray(mon.ux)\n",
    "    uy = np.asarray(mon.uy)\n",
    "    return I, ux, uy\n",
    "\n",
    "\n",
    "def collection_efficiency(I, ux, uy, na, n_bg=1.0):\n",
    "    \"\"\"\n",
    "    Fraction of total emitted power collected within NA.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    I : 2-D array  — intensity on (ux, uy) grid  (shape: n_ux × n_uy)\n",
    "    ux, uy : 1-D arrays\n",
    "    na, n_bg : float\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    float  — collection efficiency [0, 1]\n",
    "    \"\"\"\n",
    "    UX, UY = np.meshgrid(ux, uy, indexing=\"ij\")\n",
    "    R2 = UX**2 + UY**2\n",
    "    inside_na = R2 <= (na / n_bg) ** 2\n",
    "    inside_all = R2 <= 1.0\n",
    "    upper_total = np.sum(I[inside_all])\n",
    "    if upper_total == 0:\n",
    "        return 0.0\n",
    "    total_emitted = 2.0 * upper_total\n",
    "    return float(np.sum(I[inside_na]) / total_emitted)\n",
    "\n",
    "\n",
    "# ── Compute and plot ───────────────────────────────────────────────────────────\n",
    "print(\"── Far-field radiation pattern ──────────────────────────────────────────\")\n",
    "\n",
    "I_ff, ux, uy = extract_kspace_intensity(data)\n",
    "eta_NA = collection_efficiency(I_ff, ux, uy, NA, N_BG)\n",
    "print(f\"  Collection efficiency at NA = {NA}: {eta_NA * 100:.1f}%\")\n",
    "\n",
    "# NA sweep\n",
    "na_sweep = np.linspace(0.05, 0.99, 80)\n",
    "eta_sweep = [collection_efficiency(I_ff, ux, uy, na_) for na_ in na_sweep]\n",
    "\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(11.2, 4.2))\n",
    "\n",
    "# k-space map\n",
    "UX_g, UY_g = np.meshgrid(ux, uy, indexing=\"ij\")\n",
    "ax1.pcolormesh(\n",
    "    ux, uy, I_ff.T / I_ff.max(), cmap=mono_cmap, vmin=0, vmax=1, shading=\"auto\"\n",
    ")\n",
    "theta_c = np.linspace(0, 2 * np.pi, 300)\n",
    "for na_c, ls_c, lbl_c in [(NA, \"--\", f\"NA = {NA:.2f}\"), (0.9, \":\", \"NA = 0.90\")]:\n",
    "    if na_c <= 1.0:\n",
    "        ax1.plot(\n",
    "            na_c * np.cos(theta_c),\n",
    "            na_c * np.sin(theta_c),\n",
    "            color=WHITE,\n",
    "            lw=1.7,\n",
    "            ls=ls_c,\n",
    "            alpha=0.95,\n",
    "        )\n",
    "ax1.set_aspect(\"equal\")\n",
    "ax1.set_xlabel(\"$u_x = k_x/k_0$\")\n",
    "ax1.set_ylabel(\"$u_y = k_y/k_0$\")\n",
    "ax1.set_title(\"Far-Field Radiation Pattern (k-space)\")\n",
    "ax1.text(\n",
    "    0.97,\n",
    "    0.05,\n",
    "    f\"--  NA = {NA:.2f}\\n:   NA = 0.90\",\n",
    "    transform=ax1.transAxes,\n",
    "    ha=\"right\",\n",
    "    va=\"bottom\",\n",
    "    color=WHITE,\n",
    "    fontsize=9,\n",
    "    bbox=dict(boxstyle=\"round,pad=0.28\", fc=(0, 0, 0, 0.35), ec=\"none\"),\n",
    ")\n",
    "\n",
    "# Collection efficiency curve\n",
    "ax2.plot(na_sweep, np.array(eta_sweep) * 100, color=RED, lw=2)\n",
    "ax2.axvline(NA, color=BLUE, ls=\"--\", lw=1.8, label=f\"NA = {NA}  →  {eta_NA * 100:.1f}%\")\n",
    "ax2.set_xlabel(\"Numerical aperture (NA)\")\n",
    "ax2.set_ylabel(\"Collection efficiency (%)\")\n",
    "ax2.set_title(\"Collection Efficiency vs NA\")\n",
    "ax2.set_xlim(0, 1)\n",
    "ax2.set_ylim(0, 55)\n",
    "ax2.legend()\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27-polarisation-header",
   "metadata": {},
   "source": [
    "\n",
    "## 10. Polarization Analysis\n",
    "\n",
    "The polarization state of the emitted light is characterized by the **Stokes parameters**, derived from the spherical far-field components $(E_\\theta, E_\\phi)$ recorded by the [`FieldProjectionAngleMonitor`](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.FieldProjectionAngleMonitor.html):\n",
    "\n",
    "$$S_0 = |E_\\theta|^2 + |E_\\phi|^2, \\quad\n",
    "S_1 = |E_\\theta|^2 - |E_\\phi|^2, \\quad\n",
    "S_2 = 2\\,\\text{Re}(E_\\theta E_\\phi^*), \\quad\n",
    "S_3 = 2\\,\\text{Im}(E_\\theta E_\\phi^*)$$\n",
    "\n",
    "Key metrics:\n",
    "\n",
    "| Metric | Formula | Meaning |\n",
    "|--------|---------|--------|\n",
    "| DoLP | $\\sqrt{S_1^2 + S_2^2} / S_0$ | Degree of linear polarization |\n",
    "| DoCP | $|S_3| / S_0$ | Degree of circular polarization |\n",
    "| DoP | $\\sqrt{S_1^2 + S_2^2 + S_3^2} / S_0$ | Total degree of polarization |\n",
    "| $\\psi$ | $\\frac{1}{2}\\arctan(S_2/S_1)$ | Orientation angle of the polarization ellipse |"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "28-polarisation-analysis",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-04-07T18:23:07.133056Z",
     "iopub.status.busy": "2026-04-07T18:23:07.132984Z",
     "iopub.status.idle": "2026-04-07T18:23:07.253424Z",
     "shell.execute_reply": "2026-04-07T18:23:07.253093Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "── Polarisation analysis ────────────────────────────────────────────────\n",
      "  DoLP  (linear)  = 0.952\n",
      "  DoCP  (circular)= 0.221\n",
      "  DoP   (total)   = 1.000\n",
      "  ψ (orientation) = 0.5°\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "
"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def compute_stokes(data, monitor_name=\"farfield_angles\", na=0.65, n_bg=1.0):\n",
    "    \"\"\"\n",
    "    Compute Stokes parameters and polarisation metrics from the far-field.\n",
    "\n",
    "    Cartesian field components (Ex, Ey, Ez) are projected onto the spherical\n",
    "    basis (E_theta, E_phi) using the standard physics convention for the\n",
    "    e_theta and e_phi unit vectors.  Results are returned per (theta, phi)\n",
    "    angle pair and as NA-weighted scalar averages.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    data : td.SimulationData\n",
    "    monitor_name : str\n",
    "    na : float  — collection NA for weighting\n",
    "    n_bg : float\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    dict  — contains S0..S3, DoLP, DoCP, DoP, psi, chi,\n",
    "             theta/phi arrays, NA_mask, and averaged scalars\n",
    "    \"\"\"\n",
    "    mon = data[monitor_name]\n",
    "\n",
    "    theta = np.asarray(mon.theta)  # shape (n_theta,) [rad]\n",
    "    phi = np.asarray(mon.phi)  # shape (n_phi,)   [rad]\n",
    "\n",
    "    # Extract or compute spherical field components\n",
    "    if hasattr(mon, \"Etheta\"):\n",
    "        # Already in spherical basis\n",
    "        dims = mon.Etheta.dims\n",
    "        if \"r\" in dims:\n",
    "            Et = mon.Etheta.isel(f=0, r=0).values\n",
    "            Ep = mon.Ephi.isel(f=0, r=0).values\n",
    "        else:\n",
    "            Et = mon.Etheta.isel(f=0).values\n",
    "            Ep = mon.Ephi.isel(f=0).values\n",
    "    else:\n",
    "        # Project Cartesian → spherical\n",
    "        Ex = mon.Ex.isel(f=0).values.squeeze()\n",
    "        Ey = mon.Ey.isel(f=0).values.squeeze()\n",
    "        Ez = mon.Ez.isel(f=0).values.squeeze() if hasattr(mon, \"Ez\") else 0.0\n",
    "        TH, PH = np.meshgrid(theta, phi, indexing=\"ij\")\n",
    "        ct, st = np.cos(TH), np.sin(TH)\n",
    "        cp, sp = np.cos(PH), np.sin(PH)\n",
    "        Et = ct * cp * Ex + ct * sp * Ey - st * Ez\n",
    "        Ep = -sp * Ex + cp * Ey\n",
    "\n",
    "    eps = 1e-30\n",
    "    S0 = np.abs(Et) ** 2 + np.abs(Ep) ** 2\n",
    "    S1 = np.abs(Et) ** 2 - np.abs(Ep) ** 2\n",
    "    S2 = 2 * np.real(Et * np.conj(Ep))\n",
    "    S3 = 2 * np.imag(Et * np.conj(Ep))\n",
    "\n",
    "    DoLP = np.sqrt(S1**2 + S2**2) / (S0 + eps)\n",
    "    DoCP = np.abs(S3) / (S0 + eps)\n",
    "    DoP = np.sqrt(S1**2 + S2**2 + S3**2) / (S0 + eps)\n",
    "    psi = 0.5 * np.arctan2(S2, S1)\n",
    "    chi = 0.5 * np.arcsin(np.clip(S3 / (S0 + eps), -1, 1))\n",
    "\n",
    "    # NA mask and S0-weighted averages within collection cone\n",
    "    TH_g, _ = np.meshgrid(theta, phi, indexing=\"ij\")\n",
    "    theta_max = np.arcsin(min(na / n_bg, 0.9999))\n",
    "    NA_mask = TH_g <= theta_max\n",
    "    w = np.sin(TH_g)  # solid-angle weight sin(θ)\n",
    "\n",
    "    def wavg(arr):\n",
    "        wt = (S0 * w * NA_mask).sum() + eps\n",
    "        return float((arr * S0 * w * NA_mask).sum() / wt)\n",
    "\n",
    "    return {\n",
    "        \"S0\": S0,\n",
    "        \"S1\": S1,\n",
    "        \"S2\": S2,\n",
    "        \"S3\": S3,\n",
    "        \"DoLP\": DoLP,\n",
    "        \"DoCP\": DoCP,\n",
    "        \"DoP\": DoP,\n",
    "        \"psi\": psi,\n",
    "        \"chi\": chi,\n",
    "        \"theta\": theta,\n",
    "        \"phi\": phi,\n",
    "        \"NA_mask\": NA_mask,\n",
    "        \"DoLP_avg\": wavg(DoLP),\n",
    "        \"DoCP_avg\": wavg(DoCP),\n",
    "        \"DoP_avg\": wavg(DoP),\n",
    "        \"psi_avg\": wavg(psi),\n",
    "        \"theta_max_deg\": np.rad2deg(theta_max),\n",
    "    }\n",
    "\n",
    "\n",
    "def plot_stokes(pol, na):\n",
    "    \"\"\"Plot S0 intensity, DoLP, and DoCP on θ-φ heatmaps.\"\"\"\n",
    "    theta_deg = np.rad2deg(pol[\"theta\"])\n",
    "    phi_deg = np.rad2deg(pol[\"phi\"])\n",
    "\n",
    "    panels = [\n",
    "        (\n",
    "            \"$S_0$ (Intensity)\",\n",
    "            pol[\"S0\"] / pol[\"S0\"].max(),\n",
    "            mono_cmap,\n",
    "            0,\n",
    "            1,\n",
    "            \"Normalised $S_0$\",\n",
    "        ),\n",
    "        (\"DoLP\", pol[\"DoLP\"], mono_cmap, 0, 1, \"DoLP\"),\n",
    "        (\"DoCP\", pol[\"DoCP\"], mono_cmap, 0, 1, \"DoCP\"),\n",
    "    ]\n",
    "\n",
    "    fig, axes = plt.subplots(1, 3, figsize=(13.5, 4.2))\n",
    "    for ax, (title, arr, cmap, vmin, vmax, cbar_label) in zip(axes, panels):\n",
    "        im = ax.pcolormesh(\n",
    "            phi_deg, theta_deg, arr, cmap=cmap, vmin=vmin, vmax=vmax, shading=\"auto\"\n",
    "        )\n",
    "        cbar = plt.colorbar(im, ax=ax, fraction=0.03, pad=0.02)\n",
    "        cbar.set_label(cbar_label)\n",
    "        ax.axhline(pol[\"theta_max_deg\"], color=WHITE, ls=\"--\", lw=1.4, alpha=0.95)\n",
    "        ax.set_xlabel(\"$\\\\phi$ (°)\")\n",
    "        ax.set_ylabel(\"$\\\\theta$ (°)\")\n",
    "        ax.set_title(title)\n",
    "        ax.text(\n",
    "            0.97,\n",
    "            0.05,\n",
    "            f\"NA = {na:.2f}\",\n",
    "            transform=ax.transAxes,\n",
    "            ha=\"right\",\n",
    "            va=\"bottom\",\n",
    "            color=WHITE,\n",
    "            fontsize=9,\n",
    "            bbox=dict(boxstyle=\"round,pad=0.28\", fc=(0, 0, 0, 0.35), ec=\"none\"),\n",
    "        )\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "print(\"── Polarisation analysis ────────────────────────────────────────────────\")\n",
    "pol = compute_stokes(data, na=NA)\n",
    "\n",
    "print(f\"  DoLP  (linear)  = {pol['DoLP_avg']:.3f}\")\n",
    "print(f\"  DoCP  (circular)= {pol['DoCP_avg']:.3f}\")\n",
    "print(f\"  DoP   (total)   = {pol['DoP_avg']:.3f}\")\n",
    "print(f\"  ψ (orientation) = {np.rad2deg(pol['psi_avg']):.1f}°\")\n",
    "\n",
    "plot_stokes(pol, NA)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29-summary-header",
   "metadata": {},
   "source": [
    "\n",
    "## 11. Results Summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "30-summary",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-04-07T18:23:07.254842Z",
     "iopub.status.busy": "2026-04-07T18:23:07.254772Z",
     "iopub.status.idle": "2026-04-07T18:23:07.257874Z",
     "shell.execute_reply": "2026-04-07T18:23:07.257674Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==============================================================\n",
      "  DIAMOND PHOTONIC CRYSTAL NANOCAVITY – SIMULATION RESULTS\n",
      "==============================================================\n",
      "\n",
      "  Structure\n",
      "    Slab thickness         : 136.0 nm\n",
      "    Sidewall angle         : 15.6°\n",
      "    Diamond index at λ₀    : 2.4115\n",
      "\n",
      "  Resonance (from scout stage)\n",
      "    Resonance wavelength λ₀: 639.305 nm\n",
      "    Quality factor  Q      : 67767\n",
      "    Energy decay time τ    : 46.00 ps\n",
      "\n",
      "  Near-Field Confinement\n",
      "    1/e² width w_x         : 1568 nm\n",
      "    1/e² width w_y         : 197 nm\n",
      "    Eff. mode area A_eff   : 1.6626 µm² = 4.068 λ²\n",
      "\n",
      "  Mode Volume and Purcell Enhancement\n",
      "    Eff. mode volume V_eff : 0.1204 µm³ = 51.720 × (λ/2n)³\n",
      "    Purcell factor  F_P    : 796.6\n",
      "\n",
      "  Collection (NA = 0.65)\n",
      "    Collection efficiency η: 31.2%\n",
      "\n",
      "  Far-Field Polarisation\n",
      "    DoLP (linear)          : 0.952\n",
      "    DoCP (circular)        : 0.221\n",
      "    DoP  (total)           : 1.000\n",
      "    Orientation angle ψ    : 0.5°\n",
      "\n",
      "==============================================================\n"
     ]
    }
   ],
   "source": [
    "w = 62  # column width\n",
    "print(\"=\" * w)\n",
    "print(\"  DIAMOND PHOTONIC CRYSTAL NANOCAVITY – SIMULATION RESULTS\")\n",
    "print(\"=\" * w)\n",
    "\n",
    "print(\"\\n  Structure\")\n",
    "print(f\"    Slab thickness         : {THICKNESS_UM * 1e3:.1f} nm\")\n",
    "print(f\"    Sidewall angle         : {SIDEWALL_ANGLE_DEG}°\")\n",
    "print(f\"    Diamond index at λ₀    : {float(n_diamond(wavelength_lockin)):.4f}\")\n",
    "\n",
    "print(\"\\n  Resonance (from scout stage)\")\n",
    "print(f\"    Resonance wavelength λ₀: {wavelength_lockin * 1e3:.3f} nm\")\n",
    "print(f\"    Quality factor  Q      : {q_results['Q']:.0f}\")\n",
    "print(f\"    Energy decay time τ    : {q_results['decay_time_ps']:.2f} ps\")\n",
    "\n",
    "print(\"\\n  Near-Field Confinement\")\n",
    "print(f\"    1/e² width w_x         : {conf['w_x_um'] * 1e3:.0f} nm\")\n",
    "print(f\"    1/e² width w_y         : {conf['w_y_um'] * 1e3:.0f} nm\")\n",
    "print(\n",
    "    f\"    Eff. mode area A_eff   : {conf['A_eff_um2']:.4f} µm²\"\n",
    "    f\" = {conf['A_eff_lambda2']:.3f} λ²\"\n",
    ")\n",
    "\n",
    "print(\"\\n  Mode Volume and Purcell Enhancement\")\n",
    "print(f\"    Eff. mode volume V_eff : {V_eff:.4f} µm³ = {V_norm:.3f} × (λ/2n)³\")\n",
    "print(f\"    Purcell factor  F_P    : {F_P:.1f}\")\n",
    "\n",
    "print(f\"\\n  Collection (NA = {NA})\")\n",
    "print(f\"    Collection efficiency η: {eta_NA * 100:.1f}%\")\n",
    "\n",
    "print(\"\\n  Far-Field Polarisation\")\n",
    "print(f\"    DoLP (linear)          : {pol['DoLP_avg']:.3f}\")\n",
    "print(f\"    DoCP (circular)        : {pol['DoCP_avg']:.3f}\")\n",
    "print(f\"    DoP  (total)           : {pol['DoP_avg']:.3f}\")\n",
    "print(f\"    Orientation angle ψ    : {np.rad2deg(pol['psi_avg']):.1f}°\")\n",
    "\n",
    "print(\"\\n\" + \"=\" * w)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31-conclusion",
   "metadata": {},
   "source": [
    "## Conclusion\n",
    "\n",
    "This notebook demonstrates a fabrication-aware Tidy3D workflow for simulating and characterizing a diamond photonic crystal nanobeam cavity from notebook-local geometry specifications. For this fabricated cavity instance, the broadband scout plus narrowband characterization workflow predicts the cavity resonance, Q factor, mode volume, Purcell enhancement, collection efficiency, and polarization / far-field response in a compact two-stage simulation procedure.\n",
    "\n",
    "**Key takeaways:**\n",
    "\n",
    "- A broadband scout simulation combined with Tidy3D [`ResonanceFinder`](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.plugins.resonance.ResonanceFinder.html) provides an efficient route to the cavity resonance and Q estimate.\n",
    "- The narrowband characterization stage resolves near-field confinement, mode volume, Purcell enhancement, collection efficiency, and polarization response for the modeled fabricated structure.\n",
    "- Because the workflow is built from notebook-local fabrication geometry specifications, it can be reused to compare multiple cavity realizations or incorporated into larger fabrication-variation studies.\n",
    "\n",
    "---\n",
    "\n",
    "## References\n",
    "\n",
    "1. Faraon, A. *et al.* \"Resonant enhancement of the zero-phonon emission from a colour centre in a diamond cavity.\" *Nature Photonics* **5**, 301–305 (2011). doi:10.1038/nphoton.2011.52.\n",
    "2. Riedrich-Möller, J. *et al.* \"One- and two-dimensional photonic crystal microcavities in single crystal diamond.\" *Nature Nanotechnology* **7**, 69–74 (2012). doi:10.1038/nnano.2011.190.\n",
    "3. Zaitsev, A. M. *Optical Properties of Diamond: A Data Handbook.* Springer, Berlin, Heidelberg (2001). doi:10.1007/978-3-662-04548-0.\n",
    "4. Almutlaq, J., Buzzi, A., Khaykin, A., Li, L., Yzaguirre, W., Sirotin, M., Gilbert, G., Clark, G., and Englund, D. \"Foundry-Enabled Patterning of Diamond Quantum Microchiplets for Scalable Quantum Photonics.\" arXiv:2601.20025 [quant-ph] (2026). doi:10.48550/arXiv.2601.20025.\n",
    "5. Flexcompute. *Tidy3D Documentation.* https://docs.flexcompute.com/projects/tidy3d/en/latest/ (accessed 2026-04-06).\n",
    "6. Purcell, E. M. \"Spontaneous emission probabilities at radio frequencies.\" In *Proceedings of the American Physical Society*, *Physical Review* **69**, 674 (1946). doi:10.1103/PhysRev.69.674.2."
   ]
  }
 ],
 "metadata": {
  "applications": [
   "Quantum emitters"
  ],
  "description": "This notebook demonstrates how to simulate and characterize a diamond photonic crystal nanobeam cavity for solid-state quantum photonics using Tidy3D.",
  "feature_image": "./img/DiamondPC.png",
  "features": [
   "Resonance finder"
  ],
  "kernelspec": {
   "display_name": "base",
   "language": "python",
   "name": "python3"
  },
  "keywords": "diamond photonic crystal, nanobeam cavity, quantum emitters, Tidy3D, FDTD",
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.7"
  },
  "title": "How to simulate a diamond nanobeam photonic crystal cavity for quantum emitters using Tidy3D | Flexcompute"
 },
 "nbformat": 4,
 "nbformat_minor": 5
}