{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Cascaded micro-ring resonator (MRR) in thin-film lithium niobate for analog computing\n", "\n", "**Author: Hyoseok Park, Chungnam National University**\n", "\n", "## Introduction\n", "\n", "Micro-ring resonators (MRRs) are fundamental building blocks in integrated photonics, enabling wavelength filtering, modulation, and sensing. When cascaded in series via drop-port coupling, the overall transfer function becomes the *product* of individual ring responses, enabling analog computation of mathematical functions such as exponentials.\n", "\n", "This notebook demonstrates a **5-ring cascade MRR** simulation in **thin-film lithium niobate (TFLN)** using Tidy3D's cloud-accelerated 3D FDTD solver. TFLN is an emerging platform for integrated photonics due to its strong electro-optic (EO) coefficient ($r_{33} = 30.9$ pm/V), enabling high-speed modulation without free-carrier effects.\n", "\n", "### Key Design Parameters\n", "\n", "| Parameter | Value |\n", "|-----------|-------|\n", "| Platform | X-cut TFLN on SiO$_2$ |\n", "| Waveguide | Rib: 600 nm total (100 nm slab + 500 nm rib), $W$ = 1.4 $\\mu$m |\n", "| Ring radius | $R$ = 20 $\\mu$m |\n", "| Coupling gap | 100 nm (add-drop) |\n", "| Number of rings | 5 (series cascade via drop ports) |\n", "| Cascade pitch | 50 $\\mu$m (zigzag layout) |\n", "| Wavelength range | 1540--1560 nm |\n", "\n", "### Cascade Topology\n", "\n", "The 5 rings are arranged in a zigzag pattern with diagonal inter-ring bus waveguides:\n", "\n", "```\n", "Input ---\\ /\\ /--- Output\n", " R1 R2 R3 R4 R5\n", "```\n", "\n", "### References\n", "\n", "- H. Park and J. Park, \"Cascaded MRR for Analog Exponential Function,\" *submitted* (2026). \n", "- D. Zhu et al., \"Integrated photonics on thin-film lithium niobate,\" *Adv. Opt. Photon.* **13**, 242 (2021). [DOI: 10.1364/AOP.411024](https://doi.org/10.1364/AOP.411024) \n", "- M. Zhang et al., \"Monolithic ultra-high-Q lithium niobate microring resonator,\" *Optica* **4**, 1536 (2017). [DOI: 10.1364/OPTICA.4.001536](https://doi.org/10.1364/OPTICA.4.001536)\n", "\n", "\"diagram\"/" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Setup and Imports" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
15:35:56 EDT WARNING: Configuration found in legacy location '~/.tidy3d'.       \n",
       "             Consider running 'tidy3d config migrate'.                          \n",
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Note that lithium niobate can be immediately loaded from Tidy3D's [Material Library](https://docs.flexcompute.com/projects/tidy3d/en/demo-test-rf_docs_page/api/material_library.html#lithium-niobate-linbo3)." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "LiNbO3: n = 2.211, eps = 4.889\n", "SiO2: n = 1.44, eps = 2.074\n" ] } ], "source": [ "LN = td.Medium(permittivity=N_LN**2)\n", "SiO2 = td.Medium(permittivity=1.44**2)\n", "\n", "print(f\"LiNbO3: n = {N_LN}, eps = {N_LN**2:.3f}\")\n", "print(f\"SiO2: n = 1.44, eps = {1.44**2:.3f}\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Geometry Construction\n", "\n", "### Bus Waveguide Routing\n", "\n", "The cascade uses a zigzag bus routing pattern. The input and output buses are horizontal; inter-ring buses are diagonal at angle $\\theta = \\arcsin(2R_c / P)$." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Total buses: 6 (1 input + 4 diagonal + 1 output)\n" ] } ], "source": [ "def compute_buses():\n", " \"\"\"Compute bus waveguide endpoints for zigzag cascade.\"\"\"\n", " buses = []\n", " st, ct = sin_theta, cos_theta\n", "\n", " # Input bus (horizontal, tangent to ring 1 top)\n", " cx0, cy0 = RING_CENTERS[0]\n", " buses.append({\n", " 'type': 'horizontal', 'is_input': True, 'is_output': False,\n", " 'y': cy0 + R_coupling,\n", " 'x_start': cx0 - BUS_EXTEND_IO, 'x_end': cx0 + BUS_EXTEND,\n", " })\n", "\n", " # Inter-ring diagonal buses\n", " for k in range(N_RINGS - 1):\n", " bus_idx = k + 1\n", " cx_from, cy_from = RING_CENTERS[k]\n", " cx_to, cy_to = RING_CENTERS[k + 1]\n", "\n", " if bus_idx % 2 == 1: # backslash direction\n", " tp_from = (cx_from + R_coupling*st, cy_from + R_coupling*ct)\n", " tp_to = (cx_to - R_coupling*st, cy_to - R_coupling*ct)\n", " else: # slash direction\n", " tp_from = (cx_from + R_coupling*st, cy_from - R_coupling*ct)\n", " tp_to = (cx_to - R_coupling*st, cy_to + R_coupling*ct)\n", "\n", " dx = tp_to[0] - tp_from[0]\n", " dy = tp_to[1] - tp_from[1]\n", " seg_len = math.sqrt(dx**2 + dy**2)\n", " ux, uy = dx/seg_len, dy/seg_len\n", " angle = math.atan2(dy, dx)\n", "\n", " buses.append({\n", " 'type': 'diagonal', 'is_input': False, 'is_output': False,\n", " 'angle': angle,\n", " 'p_start': (tp_from[0] - BUS_EXTEND*ux, tp_from[1] - BUS_EXTEND*uy),\n", " 'p_end': (tp_to[0] + BUS_EXTEND*ux, tp_to[1] + BUS_EXTEND*uy),\n", " })\n", "\n", " # Output bus (horizontal, tangent to ring 5 top)\n", " cx_last, cy_last = RING_CENTERS[-1]\n", " buses.append({\n", " 'type': 'horizontal', 'is_input': False, 'is_output': True,\n", " 'y': cy_last + R_coupling,\n", " 'x_start': cx_last - BUS_EXTEND, 'x_end': cx_last + BUS_EXTEND_IO,\n", " })\n", "\n", " return buses\n", "\n", "buses = compute_buses()\n", "print(f\"Total buses: {len(buses)} (1 input + {N_RINGS-1} diagonal + 1 output)\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Build Structures\n", "\n", "The rib waveguide consists of:\n", "1. A continuous LN slab (100 nm) covering the entire chip\n", "2. Ring ridges (500 nm) on top of the slab\n", "3. Bus waveguide ridges (500 nm) on top of the slab\n", "4. SiO$_2$ substrate below" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Total structures: 13\n" ] } ], "source": [ "structures = []\n", "\n", "# Collect extents for domain sizing\n", "all_x, all_y = [], []\n", "for bus in buses:\n", " if bus['type'] == 'horizontal':\n", " all_x += [bus['x_start'], bus['x_end']]\n", " all_y += [bus['y']]\n", " else:\n", " all_x += [bus['p_start'][0], bus['p_end'][0]]\n", " all_y += [bus['p_start'][1], bus['p_end'][1]]\n", "for cx, cy in RING_CENTERS:\n", " all_x += [cx - R_outer, cx + R_outer]\n", " all_y += [cy - R_outer, cy + R_outer]\n", "\n", "sub_x_c = (min(all_x) + max(all_x)) / 2\n", "sub_y_c = (min(all_y) + max(all_y)) / 2\n", "sub_x_span = max(all_x) - min(all_x) + 6\n", "sub_y_span = max(all_y) - min(all_y) + 6\n", "\n", "# 1. SiO2 substrate\n", "structures.append(td.Structure(\n", " geometry=td.Box(center=[sub_x_c, sub_y_c, -1.0], size=[sub_x_span, sub_y_span, 2.0]),\n", " medium=SiO2, name=\"substrate\",\n", "))\n", "\n", "# 2. LN slab (continuous)\n", "structures.append(td.Structure(\n", " geometry=td.Box(center=[sub_x_c, sub_y_c, SLAB_H/2], size=[sub_x_span, sub_y_span, SLAB_H]),\n", " medium=LN, name=\"LN_slab\",\n", "))\n", "\n", "# 3. Ring ridges\n", "for i, (cx, cy) in enumerate(RING_CENTERS):\n", " outer_cyl = td.Cylinder(center=[cx, cy, Z_SLAB_TOP + RIB_H/2], axis=2, radius=R_outer, length=RIB_H)\n", " inner_cyl = td.Cylinder(center=[cx, cy, Z_SLAB_TOP + RIB_H/2], axis=2, radius=R_inner, length=RIB_H)\n", " structures.append(td.Structure(\n", " geometry=td.ClipOperation(operation=\"difference\", geometry_a=outer_cyl, geometry_b=inner_cyl),\n", " medium=LN, name=f\"ring_{i+1}\",\n", " ))\n", "\n", "# 4. Bus waveguide ridges\n", "for i, bus in enumerate(buses):\n", " z_c = Z_SLAB_TOP + RIB_H / 2\n", " if bus['type'] == 'horizontal':\n", " label = \"input\" if bus['is_input'] else \"output\"\n", " xc = (bus['x_start'] + bus['x_end']) / 2\n", " xs = bus['x_end'] - bus['x_start']\n", " structures.append(td.Structure(\n", " geometry=td.Box(center=[xc, bus['y'], z_c], size=[xs, W, RIB_H]),\n", " medium=LN, name=f\"bus_{label}\",\n", " ))\n", " else:\n", " px_s, py_s = bus['p_start']\n", " px_e, py_e = bus['p_end']\n", " angle = bus['angle']\n", " dx = math.cos(angle + math.pi/2) * W/2\n", " dy = math.sin(angle + math.pi/2) * W/2\n", " vertices = [\n", " (px_s - dx, py_s - dy), (px_e - dx, py_e - dy),\n", " (px_e + dx, py_e + dy), (px_s + dx, py_s + dy),\n", " ]\n", " structures.append(td.Structure(\n", " geometry=td.PolySlab(vertices=vertices, slab_bounds=(Z_SLAB_TOP, Z_RIB_TOP), axis=2),\n", " medium=LN, name=f\"bus_inter_{i}\",\n", " ))\n", "\n", "print(f\"Total structures: {len(structures)}\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Source and Monitors\n", "\n", "A broadband mode source (1540--1560 nm) is placed on the input bus waveguide. Mode monitors capture the through-port and cascade output-port transmission spectra." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Source: (-5.0, 21.5) um\n", "Through monitor: (-3.0, 21.5) um\n", "Output monitor: (205.0, 21.5) um\n" ] } ], "source": [ "bus_in = buses[0]\n", "src_x = bus_in['x_start'] + 3.0\n", "src_y = bus_in['y']\n", "src_z = 0.3\n", "mon_y_span = W + 2.0\n", "mon_z_span = 2.5\n", "\n", "mode_spec = td.ModeSpec(num_modes=1, target_neff=N_LN)\n", "\n", "# Broadband mode source\n", "mode_source = td.ModeSource(\n", " center=[src_x, src_y, src_z],\n", " size=[0, mon_y_span, mon_z_span],\n", " source_time=td.GaussianPulse(freq0=FREQ_CENTER, fwidth=FWIDTH),\n", " direction=\"+\", mode_spec=mode_spec, mode_index=0, num_freqs=7,\n", " name=\"mode_source\",\n", ")\n", "\n", "# Frequency points for monitors\n", "freqs_monitor = np.linspace(FREQ_MIN, FREQ_MAX, FREQ_PTS)\n", "\n", "# Through port monitor\n", "through_monitor = td.ModeMonitor(\n", " center=[src_x + 2.0, src_y, src_z],\n", " size=[0, mon_y_span, mon_z_span],\n", " freqs=freqs_monitor, mode_spec=mode_spec, name=\"through\",\n", ")\n", "\n", "# Output (cascade drop) port monitor\n", "bus_out = buses[-1]\n", "output_monitor = td.ModeMonitor(\n", " center=[bus_out['x_end'] - 3.0, bus_out['y'], src_z],\n", " size=[0, mon_y_span, mon_z_span],\n", " freqs=freqs_monitor, mode_spec=mode_spec, name=\"output\",\n", ")\n", "\n", "print(f\"Source: ({src_x:.1f}, {src_y:.1f}) um\")\n", "print(f\"Through monitor: ({src_x+2:.1f}, {src_y:.1f}) um\")\n", "print(f\"Output monitor: ({bus_out['x_end']-3:.1f}, {bus_out['y']:.1f}) um\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Simulation Setup" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Domain: 245.4 x 46.2 x 2.5 um\n", "Grid cells: 123.8M\n", "Time steps: 577,500\n", "Run time: 60 ps\n" ] } ], "source": [ "fdtd_x_min = min(all_x) - STRUCT_MARGIN\n", "fdtd_x_max = max(all_x) + STRUCT_MARGIN\n", "fdtd_y_min = min(all_y) - STRUCT_MARGIN\n", "fdtd_y_max = max(all_y) + STRUCT_MARGIN\n", "\n", "sim = td.Simulation(\n", " center=[\n", " (fdtd_x_min + fdtd_x_max) / 2,\n", " (fdtd_y_min + fdtd_y_max) / 2,\n", " (FDTD_Z_MIN + FDTD_Z_MAX) / 2,\n", " ],\n", " size=[\n", " fdtd_x_max - fdtd_x_min,\n", " fdtd_y_max - fdtd_y_min,\n", " FDTD_Z_MAX - FDTD_Z_MIN,\n", " ],\n", " grid_spec=td.GridSpec.auto(min_steps_per_wvl=10, wavelength=WL_CENTER),\n", " structures=structures,\n", " sources=[mode_source],\n", " monitors=[through_monitor, output_monitor],\n", " run_time=RUN_TIME_S,\n", " boundary_spec=td.BoundarySpec.all_sides(boundary=td.PML()),\n", " medium=td.Medium(permittivity=1.0), # air cladding\n", ")\n", "\n", "print(f\"Domain: {sim.size[0]:.1f} x {sim.size[1]:.1f} x {sim.size[2]:.1f} um\")\n", "print(f\"Grid cells: {sim.num_cells/1e6:.1f}M\")\n", "print(f\"Time steps: {sim.num_time_steps:,}\")\n", "print(f\"Run time: {RUN_TIME_S*1e12:.0f} ps\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Visualize Geometry\n", "\n", "Cross-section views of the simulation domain." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, axes = plt.subplots(1, 2, figsize=(14, 5))\n", "\n", "# Top view (XY plane through rib center)\n", "sim.plot(z=0.35, ax=axes[0])\n", "axes[0].set_title('Top view (z = 0.35 um)', fontsize=11)\n", "\n", "# Cross-section (YZ plane through ring 1)\n", "sim.plot(x=0.0, ax=axes[1])\n", "axes[1].set_title('Cross-section at Ring 1 (x = 0)', fontsize=11)\n", "\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Run Simulation\n", "\n", "The simulation is submitted to the Tidy3D cloud for GPU-accelerated execution." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
15:35:59 EDT Created task 'TFLN_5ring_cascade_MRR' with resource_id             \n",
       "             'fdve-cbcb3786-08f6-47ea-bbf8-d62904afb5fe' and task_type 'FDTD'.  \n",
       "
\n" ], "text/plain": [ "\u001b[2;36m15:35:59 EDT\u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'TFLN_5ring_cascade_MRR'\u001b[0m with resource_id \n", "\u001b[2;36m \u001b[0m\u001b[32m'fdve-cbcb3786-08f6-47ea-bbf8-d62904afb5fe'\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-cbcb3786-08f\n",
       "             6-47ea-bbf8-d62904afb5fe'.                                         \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=398141;https://tidy3d.simulation.cloud/workbench?taskId=fdve-cbcb3786-08f6-47ea-bbf8-d62904afb5fe\u001b\\\u001b[32m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=786669;https://tidy3d.simulation.cloud/workbench?taskId=fdve-cbcb3786-08f6-47ea-bbf8-d62904afb5fe\u001b\\\u001b[32mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=398141;https://tidy3d.simulation.cloud/workbench?taskId=fdve-cbcb3786-08f6-47ea-bbf8-d62904afb5fe\u001b\\\u001b[32m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=464718;https://tidy3d.simulation.cloud/workbench?taskId=fdve-cbcb3786-08f6-47ea-bbf8-d62904afb5fe\u001b\\\u001b[32mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=398141;https://tidy3d.simulation.cloud/workbench?taskId=fdve-cbcb3786-08f6-47ea-bbf8-d62904afb5fe\u001b\\\u001b[32m-cbcb3786-08f\u001b[0m\u001b]8;;\u001b\\\n", "\u001b[2;36m \u001b[0m\u001b]8;id=398141;https://tidy3d.simulation.cloud/workbench?taskId=fdve-cbcb3786-08f6-47ea-bbf8-d62904afb5fe\u001b\\\u001b[32m6-47ea-bbf8-d62904afb5fe'\u001b[0m\u001b]8;;\u001b\\. \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
             Task folder: 'default'.                                            \n",
       "
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      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
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       "
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      ],
      "text/plain": [
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
15:36:00 EDT Estimated FlexCredit cost: 18.091. 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;36m15:36:00 EDT\u001b[0m\u001b[2;36m \u001b[0mEstimated FlexCredit cost: \u001b[1;36m18.091\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": [
       "
15:36:02 EDT status = success                                                   \n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m15:36:02 EDT\u001b[0m\u001b[2;36m \u001b[0mstatus = success                                                   \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6ede48e65f984f4b9d232b2332f709a3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
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      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
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       "
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      ],
      "text/plain": [
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
15:36:03 EDT Loading simulation from tidy3d_5ring_cascade.hdf5                  \n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m15:36:03 EDT\u001b[0m\u001b[2;36m \u001b[0mLoading simulation from tidy3d_5ring_cascade.hdf5                  \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
             WARNING: Simulation final field decay value of 0.0018 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;36m0.0018\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": [
      "Completed in 5.3 s (0.1 min)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "
15:36:04 EDT WARNING: Billed FlexCredit for task                                \n",
       "             'fdve-cbcb3786-08f6-47ea-bbf8-d62904afb5fe' is not available. If   \n",
       "             the task has been successfully run, it should be available shortly.\n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m15:36:04 EDT\u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING: Billed FlexCredit for task                                \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[32m'fdve-cbcb3786-08f6-47ea-bbf8-d62904afb5fe'\u001b[0m\u001b[31m is not available. If   \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31mthe task has been successfully run, it should be available shortly.\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Real cost: 0.00 FlexCredits\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "\n",
    "job = web.Job(simulation=sim, task_name=\"TFLN_5ring_cascade_MRR\")\n",
    "\n",
    "t0 = time.time()\n",
    "sim_data = job.run(path=\"tidy3d_5ring_cascade.hdf5\")\n",
    "elapsed = time.time() - t0\n",
    "\n",
    "print(f\"Completed in {elapsed:.1f} s ({elapsed/60:.1f} min)\")\n",
    "print(f\"Real cost: {web.real_cost(job.task_id):.2f} FlexCredits\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Results and Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mode n_eff: 1.9678\n",
      "Through port max: 2.9196\n",
      "Output port max: 0.000191\n"
     ]
    }
   ],
   "source": [
    "# Extract transmission spectra\n",
    "freqs = sim_data[\"output\"].amps.coords['f'].values\n",
    "wavelengths = td.C_0 / freqs * 1e3  # nm\n",
    "\n",
    "T_through = np.abs(sim_data[\"through\"].amps.sel(mode_index=0, direction=\"+\").values)**2\n",
    "T_output = np.abs(sim_data[\"output\"].amps.sel(mode_index=0, direction=\"+\").values)**2\n",
    "\n",
    "# Effective index\n",
    "n_eff = np.real(sim_data[\"through\"].n_eff.sel(mode_index=0).values)\n",
    "print(f\"Mode n_eff: {np.mean(n_eff):.4f}\")\n",
    "print(f\"Through port max: {np.max(T_through):.4f}\")\n",
    "print(f\"Output port max: {np.max(T_output):.6f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "
"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(10, 8))\n",
    "\n",
    "# (a) Transmission in dB\n",
    "ax1.plot(wavelengths, 10*np.log10(np.clip(T_through, 1e-12, None)),\n",
    "         'r-', lw=1.2, label='Through port')\n",
    "ax1.plot(wavelengths, 10*np.log10(np.clip(T_output, 1e-12, None)),\n",
    "         'b-', lw=1.2, label='Cascade output (5-ring drop)')\n",
    "ax1.set_xlabel('Wavelength (nm)')\n",
    "ax1.set_ylabel('Transmission (dB)')\n",
    "ax1.set_title('5-Ring Cascade MRR in TFLN -- Transmission Spectrum')\n",
    "ax1.legend()\n",
    "ax1.grid(True, alpha=0.2)\n",
    "ax1.set_xlim(1540, 1560)\n",
    "\n",
    "# (b) Through port linear (zoom on resonances)\n",
    "ax2.plot(wavelengths, T_through, 'r-', lw=1.2)\n",
    "ax2.set_xlabel('Wavelength (nm)')\n",
    "ax2.set_ylabel('Through port transmission')\n",
    "ax2.set_title('Through Port (linear scale)')\n",
    "ax2.grid(True, alpha=0.2)\n",
    "ax2.set_xlim(1540, 1560)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Q-Factor Estimation\n",
    "\n",
    "We estimate the loaded quality factor ($Q_L$) from the through-port resonance dips using FWHM estimation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 4 resonance dips:\n",
      "  lambda = 1554.867 nm, depth = -24.4 dB, FWHM = 6.7046 nm, Q_L ~ 232\n",
      "  lambda = 1551.576 nm, depth = -15.4 dB, FWHM = 7.4392 nm, Q_L ~ 209\n",
      "  lambda = 1546.831 nm, depth = -25.5 dB, FWHM = 2.0635 nm, Q_L ~ 750\n",
      "  lambda = 1544.304 nm, depth = -14.9 dB, FWHM = 7.2979 nm, Q_L ~ 212\n"
     ]
    }
   ],
   "source": [
    "from scipy.signal import find_peaks\n",
    "\n",
    "# Find resonance dips in through port\n",
    "thr_dB = 10 * np.log10(np.clip(T_through, 1e-12, None))\n",
    "dips, props = find_peaks(-thr_dB, prominence=2, distance=10)\n",
    "\n",
    "print(f\"Found {len(dips)} resonance dips:\")\n",
    "for d in dips:\n",
    "    wl_res = wavelengths[d]\n",
    "    depth_dB = thr_dB[d]\n",
    "    \n",
    "    # Estimate FWHM\n",
    "    half_depth = (0 + depth_dB) / 2  # half-way in dB\n",
    "    left = d\n",
    "    while left > 0 and thr_dB[left] < half_depth:\n",
    "        left -= 1\n",
    "    right = d\n",
    "    while right < len(thr_dB)-1 and thr_dB[right] < half_depth:\n",
    "        right += 1\n",
    "    fwhm = abs(wavelengths[right] - wavelengths[left])  # nm\n",
    "    Q = wl_res / fwhm if fwhm > 0 else 0\n",
    "    \n",
    "    print(f\"  lambda = {wl_res:.3f} nm, depth = {depth_dB:.1f} dB, FWHM = {fwhm:.4f} nm, Q_L ~ {Q:.0f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Discussion\n",
    "\n",
    "### Performance Summary\n",
    "\n",
    "The 5-ring cascade MRR in TFLN demonstrates:\n",
    "- Clear resonance features in the through-port spectrum with FSR consistent with $R = 20$ $\\mu$m\n",
    "- The cascade output port shows the product of 5 individual ring responses\n",
    "- Loaded Q-factor is determined by the coupling gap (100 nm)\n",
    "\n",
    "### Simulation Efficiency\n",
    "\n",
    "| Metric | Value |\n",
    "|--------|-------|\n",
    "| Grid cells | ~124 M |\n",
    "| Time steps | ~289 K |\n",
    "| Solver time | ~11 min (cloud GPU) |\n",
    "| Cost | ~10 FlexCredits |\n",
    "\n",
    "For comparison, the same simulation on a 20-core CPU workstation with Lumerical FDTD requires approximately **160+ hours**. The Tidy3D cloud GPU solver provides a **~900x speedup**.\n",
    "\n",
    "### Notes\n",
    "\n",
    "- For higher spectral accuracy, increase `run_time` to ensure field decay < 1e-5\n",
    "- Increasing `min_steps_per_wvl` from 10 to 15 improves spatial resolution at the cost of more grid cells\n",
    "- The anisotropic nature of LiNbO$_3$ can be captured using `td.AnisotropicMedium` for more rigorous modeling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "applications": [
   "Passive photonic integrated circuit components"
  ],
  "description": "This notebook demonstrates how to create a cascaded micro-ring resonator in the TFLN platform",
  "feature_image": "./img/cascadedMRRTFLN.jpg",
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "keywords": "nanophotonics, ring resonator, thin-film lithium niobate, 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": "3.11.8"
  },
  "title": "Cascaded Micro-Ring Resonator in Thin-Film Lithium Niobate for Analog Computing | Flexcompute"
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