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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "82815f47-2ff1-4c34-a215-c9b428785dac",
+   "metadata": {},
+   "source": [
+    "# Transient heat analysis of thermo-optic phase-shifters\n",
+    "\n",
+    "This example demonstrates the usage of the Tidy3D's steady and unsteady heat analyses to compare the performances of thermo-optic phase-shifters based on TiN metal and N++ doped silicon in the silicon-on-insulator (SOI) platform, based on the paper [Optimization of thermo-optic phase-shifter design and mitigation of thermal crosstalk on the SOI platform\n",
+    "](https://opg.optica.org/oe/fulltext.cfm?uri=oe-27-8-10456&id=408126) by M. Jacques, A. Samani, E. El-Fiky, D. Patel, Z. Xing, and D. V. Plant.\n",
+    "\n",
+    "The two setups compared are:\n",
+    "1) A silicon strip waveguide with a TiN metal heater layer above\n",
+    "3) A silicon rib waveguide with doped silicon strips on either side\n",
+    "\n",
+    "For each design, we sweep various input heat powers to determine the power needed for a $\\pi$ phase shift ($P_\\pi$) in steady-state analysis. We then compare the times required to reach the steady-state results by running unsteady heat analyses with the required $P_\\pi$ heats. Finally, we use the $P_\\pi$ temperature profiles to optically perturb the structures and calculate the attenuations for both designs. At all stages, we achieve good agreement with the results of Jacques et. al.\n",
+    "\n",
+    "<img src=\"img/transient_thermooptic.png\" width=\"500\" alt=\"Illustration of the thermo-optic phase shifter\">"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "a13c9992-1430-4a7b-8451-4db5f3b3c83e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Standard python imports\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "\n",
+    "# Tidy3D import\n",
+    "import tidy3d as td\n",
+    "from tidy3d import web"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c935f424-f9bc-462d-ab9d-b8e917d609d7",
+   "metadata": {},
+   "source": [
+    "## Simulation Construction and Heat Input Sweep\n",
+    "\n",
+    "First we will determine which heat input results in a $\\pi$ shift for the shifter by creating a sweep of simulations, each with different heat inputs, and calculating the resulting phase shift, following from Jacques et. al."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "80369cdf-d9dd-439a-bba6-eb8c90a4e26a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "wvl0 = 1.55\n",
+    "freq0 = td.C_0 / wvl0"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cc318d40-00e2-4a59-927e-8f5eafe46232",
+   "metadata": {},
+   "source": [
+    "We define the dimensions and coordinates of the different structures involved in our simulations."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "f02c968d-94e6-4293-8175-58744b408e63",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "wg_width = 0.5\n",
+    "wg_height = 0.22\n",
+    "\n",
+    "slab_height = 0.09\n",
+    "wg_buffer = 0.8\n",
+    "slab_width = wg_width + 2 * wg_buffer\n",
+    "\n",
+    "doping_width = 1\n",
+    "\n",
+    "heater_dist = 2\n",
+    "heater_height = wg_height + heater_dist\n",
+    "heater_width = 2\n",
+    "heater_thickness = 0.2\n",
+    "\n",
+    "air_height = 2.5\n",
+    "\n",
+    "box_thickness = 2\n",
+    "\n",
+    "length = 320\n",
+    "\n",
+    "sim_size = (40, 0, 18)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b666998f-052d-4cb9-b4c8-51269d2c9135",
+   "metadata": {},
+   "source": [
+    "We define the geometries of the involved structures."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "ac5be21a-a7a6-41cd-a037-31450e382d8d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "wg_geo = td.Box(center=(0, 0, wg_height / 2), size=(wg_width, length, wg_height))\n",
+    "heater_geo = td.Box(\n",
+    "    center=(0, 0, heater_height + heater_thickness / 2),\n",
+    "    size=(heater_width, length, heater_thickness),\n",
+    ")\n",
+    "\n",
+    "rib_geo = td.Box(center=(0, 0, slab_height / 2), size=(slab_width, length, slab_height))\n",
+    "dope_L_geo = td.Box(\n",
+    "    center=((wg_width + doping_width) / 2 + wg_buffer, 0, slab_height / 2),\n",
+    "    size=(doping_width, length, slab_height),\n",
+    ")\n",
+    "dope_R_geo = td.Box(\n",
+    "    center=(-(wg_width + doping_width) / 2 - wg_buffer, 0, slab_height / 2),\n",
+    "    size=(doping_width, length, slab_height),\n",
+    ")\n",
+    "\n",
+    "air_geo = td.Box.from_bounds(\n",
+    "    rmin=(-sim_size[0] / 2, -length / 2, air_height),\n",
+    "    rmax=(sim_size[0] / 2, length / 2, air_height + length),\n",
+    ")\n",
+    "\n",
+    "box_geo = td.Box.from_bounds(\n",
+    "    rmin=(-sim_size[0] / 2, -length / 2, -box_thickness), rmax=(sim_size[0] / 2, length / 2, 0)\n",
+    ")\n",
+    "substrate_geo = td.Box.from_bounds(\n",
+    "    rmin=(-sim_size[0] / 2, -length / 2, -length),\n",
+    "    rmax=(sim_size[0] / 2, length / 2, -box_thickness),\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dfc50cdb-e62c-4550-956e-2e23901bab4a",
+   "metadata": {},
+   "source": [
+    "We have geometries that extend exactly to the simulation boundaries, but since we will only be running heat simulations and mode solving, this will not cause any problems. Thus we will suppress warnings."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "441c9c20-55d0-4ce8-ad07-aa53448b61fa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "td.config.logging_level = \"ERROR\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "28ed4163-0d60-4c6d-8f27-653c4edeb24c",
+   "metadata": {},
+   "source": [
+    "Using the material data provided in Table 3 of Jacques et. al., we define the materials as Tidy3D [`MultiPhysicsMediums`](https://docs.flexcompute.com/projects/tidy3d/en/v2.9.0/api/_autosummary/tidy3d.components.material.multi_physics.MultiPhysicsMedium.html#tidy3d.components.material.multi_physics.MultiPhysicsMedium). In these objects, we can input heat, charge, and optical properties. Our heat properties are all Tidy3D [`SolidSpec`](https://docs.flexcompute.com/projects/tidy3d/en/v2.9.0/api/_autosummary/tidy3d.SolidSpec.html#tidy3d.SolidSpec) specifications, and our optical properties are instances of Tidy3D's [`PerturbationMedium`](https://docs.flexcompute.com/projects/tidy3d/en/v2.9.0/api/_autosummary/tidy3d.PerturbationMedium.html#tidy3d.PerturbationMedium) using the provided thermo-optic coefficients. Given temperature data, these mediums will have their optical properties changed by the perturbations specified in the `PerturbationMedium`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "84575050-b141-4e9b-8d59-56611216d509",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ref_temp = 300\n",
+    "Si_dndT = 1.8e-4\n",
+    "\n",
+    "Si_perturb_undoped = td.PerturbationMedium(\n",
+    "    permittivity=3.476**2,\n",
+    "    perturbation_spec=td.IndexPerturbation(\n",
+    "        delta_n=td.ParameterPerturbation(\n",
+    "            heat=td.LinearHeatPerturbation(coeff=Si_dndT, temperature_ref=ref_temp)\n",
+    "        ),\n",
+    "        freq=freq0,\n",
+    "    ),\n",
+    ")\n",
+    "\n",
+    "n_doped, k_doped = 3.072, 0.137\n",
+    "Si_perturb_doped = Si_perturb_undoped.updated_copy(\n",
+    "    permittivity=n_doped**2 - k_doped**2,\n",
+    "    conductivity=2 * n_doped * k_doped * 2 * np.pi * freq0 * td.EPSILON_0,\n",
+    ")\n",
+    "\n",
+    "\n",
+    "SiO2_perturb = td.PerturbationMedium(\n",
+    "    permittivity=1.55**2,\n",
+    "    perturbation_spec=td.IndexPerturbation(\n",
+    "        delta_n=td.ParameterPerturbation(\n",
+    "            heat=td.LinearHeatPerturbation(coeff=1e-5, temperature_ref=ref_temp)\n",
+    "        ),\n",
+    "        freq=freq0,\n",
+    "    ),\n",
+    ")\n",
+    "\n",
+    "\n",
+    "Si_bulk = td.MultiPhysicsMedium(\n",
+    "    name=\"Si bulk\",\n",
+    "    optical=Si_perturb_undoped,\n",
+    "    heat=td.SolidSpec(conductivity=148e-6, capacity=711, density=2330e-18),\n",
+    ")\n",
+    "\n",
+    "Si_wg = td.MultiPhysicsMedium(\n",
+    "    name=\"Si wg\",\n",
+    "    optical=Si_perturb_undoped,\n",
+    "    heat=td.SolidSpec(conductivity=90e-6, capacity=711, density=2330e-18),\n",
+    ")\n",
+    "\n",
+    "Si_slab = td.MultiPhysicsMedium(\n",
+    "    name=\"Si slab\",\n",
+    "    optical=Si_perturb_undoped,\n",
+    "    heat=td.SolidSpec(conductivity=55e-6, capacity=711, density=2330e-18),\n",
+    ")\n",
+    "\n",
+    "N_Si = td.MultiPhysicsMedium(\n",
+    "    name=\"Si doped\",\n",
+    "    optical=Si_perturb_doped,\n",
+    "    heat=td.SolidSpec(conductivity=25e-6, capacity=711, density=2330e-18),\n",
+    ")\n",
+    "\n",
+    "SiO2 = td.MultiPhysicsMedium(\n",
+    "    name=\"SiO2\",\n",
+    "    optical=SiO2_perturb,\n",
+    "    heat=td.SolidSpec(conductivity=1.38e-6, capacity=709, density=2203e-18),\n",
+    ")\n",
+    "\n",
+    "TiN = td.MultiPhysicsMedium(\n",
+    "    name=\"TiN\", heat=td.SolidSpec(conductivity=28e-6, capacity=598, density=5240e-18)\n",
+    ")\n",
+    "\n",
+    "air_medium = td.MultiPhysicsMedium(\n",
+    "    name=\"air\",\n",
+    "    heat=td.FluidSpec(thermal_conductivity=0.026e-6, specific_heat=1006, density=1.177e-18),\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c6d3a1ca-f4a0-4485-bcf4-cd81026e1db5",
+   "metadata": {},
+   "source": [
+    "We put the geometries and mediums together in Tidy3D `Structure` objects."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "451f4c2e-9903-4ddc-9fe4-84ae43f1f0d0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "wg = td.Structure(geometry=wg_geo, medium=Si_wg)\n",
+    "heater = td.Structure(geometry=heater_geo, medium=TiN, name=\"heater TiN\")\n",
+    "\n",
+    "rib = td.Structure(geometry=rib_geo, medium=Si_slab)\n",
+    "doped_wgs = td.Structure(geometry=dope_L_geo + dope_R_geo, medium=N_Si, name=\"heater doped\")\n",
+    "\n",
+    "air = td.Structure(geometry=air_geo, medium=air_medium)\n",
+    "\n",
+    "box = td.Structure(geometry=box_geo, medium=SiO2)\n",
+    "substrate = td.Structure(geometry=substrate_geo, medium=Si_bulk)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c407b193-9ce9-42b7-b779-d543645953c0",
+   "metadata": {},
+   "source": [
+    "We add our structures into a [`Scene`](https://docs.flexcompute.com/projects/tidy3d/en/v2.9.0/api/_autosummary/tidy3d.Scene.html#tidy3d.Scene) object. This is a convenient way to package our structures and allows for easy conversion to HeatCharge simulations."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "c96fe169-a312-4bd9-8e44-18a65bad6a32",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "wg_scene = td.Scene(medium=SiO2, structures=[substrate, box, wg, air, heater])\n",
+    "\n",
+    "rib_scene = td.Scene(medium=SiO2, structures=[substrate, box, rib, wg, air, doped_wgs])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "009787d5-486b-43b3-83ca-71ffa3c26006",
+   "metadata": {},
+   "source": [
+    "We plot the scenes to make sure our structures are set up correctly."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "e784ea24-7b96-4b03-b4d9-2a226b47f950",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(1, 2, figsize=(15, 5))\n",
+    "\n",
+    "wg_scene.plot(y=0, hlim=[-4, 4], vlim=[-3, air_height + 1], ax=ax[0])\n",
+    "rib_scene.plot(y=0, hlim=[-4, 4], vlim=[-3, air_height + 1], ax=ax[1])\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "17865fa6-6ee6-4b96-8696-f4440d473720",
+   "metadata": {},
+   "source": [
+    "We will measure the temperature in the waveguide by a point temperature monitor located at the center of the waveguides."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "fbf0b8e2-6461-4d33-9db4-5a6d1840e360",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "temp_mnt_pnt = td.TemperatureMonitor(\n",
+    "    center=(0, 0, wg_height / 2),\n",
+    "    size=(0, 0, 0),\n",
+    "    name=\"temperature\",\n",
+    "    unstructured=True,\n",
+    "    conformal=True,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "438349b7-2cdc-4d38-913d-cfb3d905549d",
+   "metadata": {},
+   "source": [
+    "We define boundary and meshing specs for our heat simulations. Jacques et. al. give the bottom simulation boundary a fixed temperature of 300 K and a constant convection of $10 \\frac{W}{m^2}K$ (or $10e-12  \\frac{W}{μm^2}K$) between the silicon dioxide cladding and air."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "60a64b43-8917-4d73-a4d6-f5b60f475c7e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# boundary conditions\n",
+    "bc_bottom = td.HeatBoundarySpec(\n",
+    "    placement=td.SimulationBoundary(surfaces=[\"z-\"]),\n",
+    "    condition=td.TemperatureBC(temperature=ref_temp),\n",
+    ")\n",
+    "\n",
+    "bc_top = td.HeatBoundarySpec(\n",
+    "    placement=td.MediumMediumInterface(mediums=[\"air\", \"SiO2\"]),\n",
+    "    condition=td.ConvectionBC(ambient_temperature=ref_temp, transfer_coeff=10e-12),  # W/um^2 K\n",
+    ")\n",
+    "\n",
+    "# parameters for meshing\n",
+    "dl_min = heater_thickness / 3\n",
+    "dl_max = 4 * dl_min\n",
+    "\n",
+    "grid_spec = td.DistanceUnstructuredGrid(\n",
+    "    dl_interface=dl_min,\n",
+    "    dl_bulk=dl_max,\n",
+    "    distance_interface=3 * dl_min,\n",
+    "    distance_bulk=2 * heater_dist,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "147f086a-fffa-4392-9aaa-ec4f729d01f9",
+   "metadata": {},
+   "source": [
+    "We can now run a parameter sweep of different heat inputs to determine relationship between the input heat and the phase change along the shifter.\n",
+    "We will define functions that take as input the heater input power and output the heat simulations for the strip and rib waveguide structures, allowing us to quickly and methodically define multiple heat simulations corresponding to multiple heat inputs."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "ddd0425c-1f26-4a99-9cf3-b794e287abd0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def make_wg_sim(power):  # input mW\n",
+    "    power_rate = power * 1e-3 / heater_geo.volume()\n",
+    "    heater_TiN = td.HeatSource(rate=power_rate, structures=[heater.name])\n",
+    "    wg_sim = td.HeatChargeSimulation.from_scene(\n",
+    "        scene=wg_scene,\n",
+    "        center=(0, 0, 1),\n",
+    "        size=sim_size,\n",
+    "        boundary_spec=[bc_bottom, bc_top],\n",
+    "        sources=[heater_TiN],\n",
+    "        monitors=[temp_mnt_pnt],\n",
+    "        symmetry=(1, 0, 0),\n",
+    "        grid_spec=grid_spec,\n",
+    "    )\n",
+    "    return wg_sim\n",
+    "\n",
+    "\n",
+    "def make_rib_sim(power):  # input mW\n",
+    "    power_rate = power * 1e-3 / doped_wgs.geometry.volume()\n",
+    "    heater_doped = td.HeatSource(rate=power_rate, structures=[doped_wgs.name])\n",
+    "    rib_sim = td.HeatChargeSimulation.from_scene(\n",
+    "        scene=rib_scene,\n",
+    "        center=(0, 0, 1),  # (0, 0, 5),\n",
+    "        size=sim_size,\n",
+    "        boundary_spec=[bc_bottom, bc_top],\n",
+    "        sources=[heater_doped],\n",
+    "        monitors=[temp_mnt_pnt],\n",
+    "        symmetry=(1, 0, 0),\n",
+    "        grid_spec=grid_spec,\n",
+    "    )\n",
+    "    return rib_sim"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "5c1510a6-525a-4637-877f-fd1784e689bb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "heater_sweep = np.linspace(0, 30, 5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "29f4184a-0598-457a-aaa3-8ca0457cf3a9",
+   "metadata": {},
+   "source": [
+    "To create a batch, we will create a dictionary of simulations of the various heater rates."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "af74ff65-ecb9-4a07-8367-b1d6edb74a35",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "initial_wg_sweep = {}\n",
+    "initial_rib_sweep = {}\n",
+    "\n",
+    "for i, heat in enumerate(heater_sweep):\n",
+    "    initial_wg_sweep[str(i)] = make_wg_sim(heat)\n",
+    "\n",
+    "for i, heat in enumerate(heater_sweep):\n",
+    "    heat_rate = heat\n",
+    "    initial_rib_sweep[str(i)] = make_rib_sim(heat)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b8eb75b7-ed9f-4fb7-9c25-0c120f7f3640",
+   "metadata": {},
+   "source": [
+    "We can now create and run the batches."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "f3c81925-c862-4d2b-857f-72d7461417a0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "wg_batch = web.Batch(simulations=initial_wg_sweep, folder_name=\"thermo-optic_shifter\")\n",
+    "rib_batch = web.Batch(simulations=initial_rib_sweep, folder_name=\"thermo-optic_shifter\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "b03a113c-9610-4c3a-9b34-b46ff65c21fa",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4387999e4ba146ac9c3b8cd8fadff6d8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">18:25:29 EDT </span>Started working on Batch containing <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">5</span> tasks.                       \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m18:25:29 EDT\u001b[0m\u001b[2;36m \u001b[0mStarted working on Batch containing \u001b[1;36m5\u001b[0m tasks.                       \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">18:25:31 EDT </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.125</span> for the whole batch.                \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m18:25:31 EDT\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m0.125\u001b[0m for the whole batch.                \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Use <span style=\"color: #008000; text-decoration-color: #008000\">'Batch.real_cost()'</span> to get the billed FlexCredit cost after the\n",
+       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Batch has completed.                                               \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mUse \u001b[32m'Batch.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m to get the billed FlexCredit cost after the\n",
+       "\u001b[2;36m             \u001b[0mBatch has completed.                                               \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "db66d946b99d4bafaaf5c586822a8694",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">18:25:33 EDT </span>Batch complete.                                                    \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m18:25:33 EDT\u001b[0m\u001b[2;36m \u001b[0mBatch complete.                                                    \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "31e966f1d1b443518514af17464a602c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "915182f2add64eb0a7772ab7299150b2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">18:25:36 EDT </span>Started working on Batch containing <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">5</span> tasks.                       \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m18:25:36 EDT\u001b[0m\u001b[2;36m \u001b[0mStarted working on Batch containing \u001b[1;36m5\u001b[0m tasks.                       \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">18:25:39 EDT </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.125</span> for the whole batch.                \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m18:25:39 EDT\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m0.125\u001b[0m for the whole batch.                \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Use <span style=\"color: #008000; text-decoration-color: #008000\">'Batch.real_cost()'</span> to get the billed FlexCredit cost after the\n",
+       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Batch has completed.                                               \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mUse \u001b[32m'Batch.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m to get the billed FlexCredit cost after the\n",
+       "\u001b[2;36m             \u001b[0mBatch has completed.                                               \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cb0d4dc6a8d54059ac7271a52b70603c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">18:25:41 EDT </span>Batch complete.                                                    \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m18:25:41 EDT\u001b[0m\u001b[2;36m \u001b[0mBatch complete.                                                    \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c4d6a8a59079426089bb36305e4f8279",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "wg_batch_data = wg_batch.run(path_dir=\"thermo-optic_shifter\")\n",
+    "rib_batch_data = rib_batch.run(path_dir=\"thermo-optic_shifter\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "480ed49a-1521-4c47-a790-b0924859752c",
+   "metadata": {},
+   "source": [
+    "We now calculate the phase shift ($\\Phi$) of the shifter based on the temperature recorded in the waveguide for each heat simulation:\n",
+    "<center>$\\Delta\\Phi=\\frac{2\\pi L}{\\lambda_0}\\frac{dn_{Si}}{dT}\\Delta T$</center>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "82444208-244e-4c4f-aab6-28537c82b990",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "wg_ts, rib_ts = [], []\n",
+    "phases_wg, phases_rib = [], []\n",
+    "\n",
+    "for i in range(len(heater_sweep)):\n",
+    "    coef = 2 * np.pi * length / wvl0 * Si_dndT\n",
+    "\n",
+    "    t_wg = np.squeeze(wg_batch_data[str(i)][\"temperature\"].temperature.values) - ref_temp\n",
+    "\n",
+    "    wg_ts.append(t_wg)\n",
+    "    phases_wg.append(coef * t_wg)\n",
+    "\n",
+    "    t_rib = np.squeeze(rib_batch_data[str(i)][\"temperature\"].temperature.values) - ref_temp\n",
+    "\n",
+    "    rib_ts.append(t_rib)\n",
+    "    phases_rib.append(coef * t_rib)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "90773cf4-2712-42b6-852c-50ccc348cf5f",
+   "metadata": {},
+   "source": [
+    "We plot the results of the heat sweep: calculated phase shift vs. the heat applied to the heaters. Interpolating these results gives us the needed heat source to achieve a $\\pi$-shift in the shifter."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "ebaad286-e27e-4ddb-8161-a1959543fd1a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(heater_sweep, phases_wg, label=\"TiN\", color=\"red\", linewidth=3)\n",
+    "plt.plot(heater_sweep, phases_rib, label=\"N$_{++}$\", color=\"blue\", linewidth=3)\n",
+    "plt.plot([heater_sweep[0], heater_sweep[-1]], [np.pi, np.pi], color=\"black\", linestyle=\"--\")\n",
+    "plt.legend()\n",
+    "plt.xlim(heater_sweep[0], heater_sweep[-1])\n",
+    "plt.ylim(0, 4)\n",
+    "plt.xlabel(\"Heater power [mW]\")\n",
+    "plt.ylabel(\"Phase [rad]\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "841959c1-43f5-4b8d-b90d-28757f5f9fea",
+   "metadata": {},
+   "source": [
+    "## $\\pi$-shift Input Power\n",
+    "\n",
+    "Now we will use the paper's calculated power values that need to be applied for a full $\\pi$-shift ($P_\\pi$). We wish to display the temperature distribution along a 2D section of the simulation, so we will define a new temperature monitor. We will then ultilize the above function to create the corresponding heat simulation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "8065a817-cdd5-4f67-9dee-c2ca7119553e",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "wg_pi_shift = 23.9\n",
+    "rib_pi_shift = 25.2\n",
+    "\n",
+    "# add 2D monitor to display 2D temperature distribution\n",
+    "temp_mnt_2D = td.TemperatureMonitor(\n",
+    "    center=(0, 0, 1), size=(30, 0, 4), name=\"temperature\", unstructured=True, conformal=True\n",
+    ")\n",
+    "\n",
+    "wg_sim = make_wg_sim(wg_pi_shift).updated_copy(monitors=[temp_mnt_2D])\n",
+    "rib_sim = make_rib_sim(rib_pi_shift).updated_copy(monitors=[temp_mnt_2D])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "338903a8-ec4d-46a1-88bc-c17b52b7f23e",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">18:25:51 EDT </span>Created task <span style=\"color: #008000; text-decoration-color: #008000\">'wg'</span> with resource_id                                 \n",
+       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'hec-d5ec8930-571d-4983-975a-5636bf196d34'</span> and task_type           \n",
+       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'HEAT_CHARGE'</span>.                                                     \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m18:25:51 EDT\u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'wg'\u001b[0m with resource_id                                 \n",
+       "\u001b[2;36m             \u001b[0m\u001b[32m'hec-d5ec8930-571d-4983-975a-5636bf196d34'\u001b[0m and task_type           \n",
+       "\u001b[2;36m             \u001b[0m\u001b[32m'HEAT_CHARGE'\u001b[0m.                                                     \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Tidy3D's HeatCharge solver is currently in the beta stage. Cost of \n",
+       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>HeatCharge simulations is subject to change in the future.         \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mTidy3D's HeatCharge solver is currently in the beta stage. Cost of \n",
+       "\u001b[2;36m             \u001b[0mHeatCharge simulations is subject to change in the future.         \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a4e5e7d68e8d41c0b764cd8340ab11b6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">18:25:52 EDT </span>Estimated FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.025</span>. Minimum cost depends on task     \n",
+       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>execution details. Use <span style=\"color: #008000; text-decoration-color: #008000\">'web.real_cost(task_id)'</span> to get the billed  \n",
+       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>FlexCredit cost after a simulation run.                            \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m18:25:52 EDT\u001b[0m\u001b[2;36m \u001b[0mEstimated FlexCredit cost: \u001b[1;36m0.025\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": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">18:25:53 EDT </span>status = success                                                   \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m18:25:53 EDT\u001b[0m\u001b[2;36m \u001b[0mstatus = success                                                   \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5c1b4e42c28241a7a80312d794cfc590",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">18:25:54 EDT </span>loading simulation from thermo-optic_shifter/wg.hdf5               \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m18:25:54 EDT\u001b[0m\u001b[2;36m \u001b[0mloading simulation from thermo-optic_shifter/wg.hdf5               \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Created task <span style=\"color: #008000; text-decoration-color: #008000\">'rib'</span> with resource_id                                \n",
+       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'hec-b90afcbb-b249-49eb-9b3d-d8c33d42efd5'</span> and task_type           \n",
+       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'HEAT_CHARGE'</span>.                                                     \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'rib'\u001b[0m with resource_id                                \n",
+       "\u001b[2;36m             \u001b[0m\u001b[32m'hec-b90afcbb-b249-49eb-9b3d-d8c33d42efd5'\u001b[0m and task_type           \n",
+       "\u001b[2;36m             \u001b[0m\u001b[32m'HEAT_CHARGE'\u001b[0m.                                                     \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Tidy3D's HeatCharge solver is currently in the beta stage. Cost of \n",
+       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>HeatCharge simulations is subject to change in the future.         \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mTidy3D's HeatCharge solver is currently in the beta stage. Cost of \n",
+       "\u001b[2;36m             \u001b[0mHeatCharge simulations is subject to change in the future.         \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "875889e611104176a99fc328d6901833",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">18:25:55 EDT </span>Estimated FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.025</span>. Minimum cost depends on task     \n",
+       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>execution details. Use <span style=\"color: #008000; text-decoration-color: #008000\">'web.real_cost(task_id)'</span> to get the billed  \n",
+       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>FlexCredit cost after a simulation run.                            \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m18:25:55 EDT\u001b[0m\u001b[2;36m \u001b[0mEstimated FlexCredit cost: \u001b[1;36m0.025\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": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">18:25:56 EDT </span>status = success                                                   \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m18:25:56 EDT\u001b[0m\u001b[2;36m \u001b[0mstatus = success                                                   \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4b2281351ffa410199374a4b0a10f40e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">18:25:57 EDT </span>loading simulation from thermo-optic_shifter/rib.hdf5              \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m18:25:57 EDT\u001b[0m\u001b[2;36m \u001b[0mloading simulation from thermo-optic_shifter/rib.hdf5              \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "wg_data = web.run(wg_sim, task_name=\"wg\", path=\"thermo-optic_shifter/wg.hdf5\")\n",
+    "rib_data = web.run(rib_sim, task_name=\"rib\", path=\"thermo-optic_shifter/rib.hdf5\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7cd30753-ec30-4592-a0e9-0bb7871bde4f",
+   "metadata": {},
+   "source": [
+    "We will now plot the temperature distribution with the $P_\\pi$ powers applied to the respective heaters of the simulation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "729c705f-99cf-4c4e-9824-22a800ed78fd",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x500 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(1, 2, figsize=(15, 5))\n",
+    "wg_data[\"temperature\"].temperature.plot(grid=False, cmap=\"jet\", ax=ax[0])\n",
+    "wg_data.simulation.plot_structures(y=0, fill=False, ax=ax[0])\n",
+    "rib_data[\"temperature\"].temperature.plot(grid=False, cmap=\"jet\", ax=ax[1])\n",
+    "rib_data.simulation.plot_structures(y=0, fill=False, ax=ax[1])\n",
+    "ax[0].set_xlim(-4, 4)\n",
+    "ax[0].set_ylim(-1, 2.5)\n",
+    "ax[1].set_xlim(-4, 4)\n",
+    "ax[1].set_ylim(-1, 2.5)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ad671dfa-a4ae-49ae-8428-cd1b05bef891",
+   "metadata": {},
+   "source": [
+    "We also plot the temperature profile as a function of distance from the center of the waveguides."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "b3503466-cda2-473c-8490-91db76d96e1c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "wg_line = (\n",
+    "    wg_data[\"temperature\"].temperature.plane_slice(axis=2, pos=wg_height / 2).sel(x=slice(0, 15))\n",
+    ")\n",
+    "rib_line = (\n",
+    "    rib_data[\"temperature\"].temperature.plane_slice(axis=2, pos=wg_height / 2).sel(x=slice(0, 15))\n",
+    ")\n",
+    "\n",
+    "plt.plot(wg_line.x, np.squeeze(wg_line.values), label=\"TiN\", color=\"red\", linewidth=3)\n",
+    "plt.plot(rib_line.x, np.squeeze(rib_line.values), label=\"N$_{++}$\", color=\"blue\", linewidth=3)\n",
+    "plt.legend()\n",
+    "plt.xlim(0, 15)\n",
+    "plt.ylim(300, 316)\n",
+    "plt.xlabel(\"Distance from waveguide center in x [μm]\")\n",
+    "plt.ylabel(\"T [K]\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6160f6b6-36fb-4fcd-8793-f5e292b77b2b",
+   "metadata": {},
+   "source": [
+    "## Transient Heat Simulation\n",
+    "\n",
+    "The previous heat simulations were steady-state heat simulations. To determine the temperature profile starting from the reference temperature while the heater heats the simulation, we will need to define an [`UnsteadyHeatAnalysis`](https://docs.flexcompute.com/projects/tidy3d/en/v2.9.0/api/_autosummary/tidy3d.UnsteadyHeatAnalysis.html#tidy3d.UnsteadyHeatAnalysis) spec to our heat simulation.\n",
+    "<br><br>\n",
+    "Our `UnsteadyHeatAnalysis` spec calculates the heat distribution at a set number of timesteps until it reaches the specified simulation time. The spec then takes as input the initial temperature, then an [`UnsteadySpec`](https://docs.flexcompute.com/projects/tidy3d/en/v2.9.0/api/_autosummary/tidy3d.UnsteadySpec.html#tidy3d.UnsteadySpec) which specifies the length of time for each timestep in the analysis and the total simulation time."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "5a1e52cc-9da6-4a18-a54c-3db9659b8971",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "heat_sim_time = 60e-6\n",
+    "heat_time_step = 1e-6\n",
+    "\n",
+    "turn_on = td.UnsteadyHeatAnalysis(\n",
+    "    initial_temperature=ref_temp,\n",
+    "    unsteady_spec=td.UnsteadySpec(\n",
+    "        time_step=heat_time_step, total_time_steps=heat_sim_time / heat_time_step\n",
+    "    ),\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e99c98a4-70dd-415a-8634-b508949c1186",
+   "metadata": {},
+   "source": [
+    "Add the UnsteadyHeatAnalysis object to the heat simulation to make it a transient heat simulation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "2ea7ea5d-76fa-4c1d-b0f4-4a3abbc78947",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "wg_on = wg_sim.updated_copy(analysis_spec=turn_on)\n",
+    "rib_on = rib_sim.updated_copy(analysis_spec=turn_on)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "c8e32f6a-0341-43b8-82b7-89f9c978f321",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">18:25:58 EDT </span>Created task <span style=\"color: #008000; text-decoration-color: #008000\">'wg on'</span> with resource_id                              \n",
+       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'hec-dde9e312-b850-41ff-b58c-77c735e9ea40'</span> and task_type           \n",
+       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'HEAT_CHARGE'</span>.                                                     \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m18:25:58 EDT\u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'wg on'\u001b[0m with resource_id                              \n",
+       "\u001b[2;36m             \u001b[0m\u001b[32m'hec-dde9e312-b850-41ff-b58c-77c735e9ea40'\u001b[0m and task_type           \n",
+       "\u001b[2;36m             \u001b[0m\u001b[32m'HEAT_CHARGE'\u001b[0m.                                                     \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Tidy3D's HeatCharge solver is currently in the beta stage. Cost of \n",
+       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>HeatCharge simulations is subject to change in the future.         \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mTidy3D's HeatCharge solver is currently in the beta stage. Cost of \n",
+       "\u001b[2;36m             \u001b[0mHeatCharge simulations is subject to change in the future.         \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fac0aaf2303543ceabdb1b8ce9afb0a9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">18:25:59 EDT </span>Estimated FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.025</span>. Minimum cost depends on task     \n",
+       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>execution details. Use <span style=\"color: #008000; text-decoration-color: #008000\">'web.real_cost(task_id)'</span> to get the billed  \n",
+       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>FlexCredit cost after a simulation run.                            \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m18:25:59 EDT\u001b[0m\u001b[2;36m \u001b[0mEstimated FlexCredit cost: \u001b[1;36m0.025\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": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>status = success                                                   \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mstatus = success                                                   \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fa36efe11c2149c18055ed64bffba8f2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">18:26:00 EDT </span>loading simulation from thermo-optic_shifter/wg_on.hdf5            \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m18:26:00 EDT\u001b[0m\u001b[2;36m \u001b[0mloading simulation from thermo-optic_shifter/wg_on.hdf5            \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">18:26:01 EDT </span>Created task <span style=\"color: #008000; text-decoration-color: #008000\">'rib on'</span> with resource_id                             \n",
+       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'hec-430a348e-e776-4d90-8a25-1c6b488038e2'</span> and task_type           \n",
+       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'HEAT_CHARGE'</span>.                                                     \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m18:26:01 EDT\u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'rib on'\u001b[0m with resource_id                             \n",
+       "\u001b[2;36m             \u001b[0m\u001b[32m'hec-430a348e-e776-4d90-8a25-1c6b488038e2'\u001b[0m and task_type           \n",
+       "\u001b[2;36m             \u001b[0m\u001b[32m'HEAT_CHARGE'\u001b[0m.                                                     \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Tidy3D's HeatCharge solver is currently in the beta stage. Cost of \n",
+       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>HeatCharge simulations is subject to change in the future.         \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mTidy3D's HeatCharge solver is currently in the beta stage. Cost of \n",
+       "\u001b[2;36m             \u001b[0mHeatCharge simulations is subject to change in the future.         \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4fae6b31ce134932b2a89c219593f72d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">18:26:02 EDT </span>Estimated FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.025</span>. Minimum cost depends on task     \n",
+       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>execution details. Use <span style=\"color: #008000; text-decoration-color: #008000\">'web.real_cost(task_id)'</span> to get the billed  \n",
+       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>FlexCredit cost after a simulation run.                            \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m18:26:02 EDT\u001b[0m\u001b[2;36m \u001b[0mEstimated FlexCredit cost: \u001b[1;36m0.025\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": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>status = success                                                   \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mstatus = success                                                   \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2ad7ad5a89434937947b296cc8694648",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">18:26:03 EDT </span>loading simulation from thermo-optic_shifter/rib_on.hdf5           \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m18:26:03 EDT\u001b[0m\u001b[2;36m \u001b[0mloading simulation from thermo-optic_shifter/rib_on.hdf5           \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "wg_on_data = web.run(wg_on, task_name=\"wg on\", path=\"thermo-optic_shifter/wg_on.hdf5\")\n",
+    "rib_on_data = web.run(rib_on, task_name=\"rib on\", path=\"thermo-optic_shifter/rib_on.hdf5\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c75486a9-ad33-45d1-adf5-5dd226b65c3a",
+   "metadata": {},
+   "source": [
+    "We plot the change in temperature fraction of $\\Delta T_\\pi$ vs time. Dashed and full lines represent $1/e$ temperature changes for turn-on and turn-off heat sources from the paper."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "72f68048-8cff-4210-b7ce-7ebea2835369",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "delta_T_pi = wvl0 / (2 * length * Si_dndT)\n",
+    "\n",
+    "wg_t_time = wg_on_data.data[0].temperature.sel(x=0, z=0)\n",
+    "rib_t_time = rib_on_data.data[0].temperature.sel(x=0, z=0)\n",
+    "\n",
+    "plt.plot(\n",
+    "    wg_t_time.t,\n",
+    "    (np.squeeze(wg_t_time.values) - ref_temp) / delta_T_pi,\n",
+    "    label=\"TiN\",\n",
+    "    color=\"red\",\n",
+    "    linewidth=3,\n",
+    "    linestyle=\"--\",\n",
+    ")\n",
+    "plt.plot(\n",
+    "    rib_t_time.t,\n",
+    "    (np.squeeze(rib_t_time.values) - ref_temp) / delta_T_pi,\n",
+    "    label=\"N$_{++}$\",\n",
+    "    color=\"blue\",\n",
+    "    linewidth=3,\n",
+    "    linestyle=\"--\",\n",
+    ")\n",
+    "plt.plot([0, heat_sim_time * 1e6], [1 - 1 / np.e, 1 - 1 / np.e], color=\"black\", linestyle=\"--\")\n",
+    "plt.plot([0, heat_sim_time * 1e6], [1 / np.e, 1 / np.e], color=\"black\")\n",
+    "plt.legend()\n",
+    "plt.xlim(0, heat_sim_time * 1e6)\n",
+    "plt.ylim(0, 1)\n",
+    "plt.xlabel(\"Time [μs]\")\n",
+    "plt.ylabel(r\"$\\Delta T/\\Delta T_\\pi$\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "658f0c6d-01e8-41d6-b769-86dd230978fa",
+   "metadata": {},
+   "source": [
+    "To plot the temperature distribution through time, we can add an ipywidget slider to display the temperature distribution at various times."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "5347972d-c318-4692-a001-159868872b0c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "37851da7bcfd47a7a334079c202378cd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(FloatSlider(value=0.0, description='x', max=60.0, readout_format='.3f', step=None), Outp…"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import ipywidgets as widgets\n",
+    "\n",
+    "times = np.linspace(0, 60, 61)\n",
+    "\n",
+    "field_slider = widgets.FloatSlider(\n",
+    "    value=0,\n",
+    "    min=times[0],\n",
+    "    max=times[-1],\n",
+    "    step=None,\n",
+    "    disabled=False,\n",
+    "    continuous_update=True,\n",
+    "    orientation=\"horizontal\",\n",
+    "    readout=True,\n",
+    "    readout_format=\".3f\",\n",
+    ")\n",
+    "\n",
+    "\n",
+    "def update_wg_plot(x):\n",
+    "    if x not in times:\n",
+    "        x = min(times, key=lambda ll: abs(ll - x))\n",
+    "    wg_on_data[\"temperature\"].temperature.sel(t=x, method=\"nearest\").plot(\n",
+    "        grid=False, cmap=\"jet\", vmax=np.max(wg_on_data[\"temperature\"].temperature.values)\n",
+    "    )\n",
+    "    plt.xlim(-5, 5)\n",
+    "    plt.show()\n",
+    "\n",
+    "\n",
+    "widgets.interactive(update_wg_plot, x=field_slider)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "6c098a3f-8025-4a47-9b77-31b202e7321e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0200a77de2a74072856bae20b64fd080",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(FloatSlider(value=0.0, description='x', max=60.0, readout_format='.3f', step=None), Outp…"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def update_rib_plot(x):\n",
+    "    if x not in times:\n",
+    "        x = min(times, key=lambda ll: abs(ll - x))\n",
+    "    rib_on_data[\"temperature\"].temperature.sel(t=x, method=\"nearest\").plot(\n",
+    "        grid=False, cmap=\"jet\", vmax=np.max(rib_on_data[\"temperature\"].temperature.values)\n",
+    "    )\n",
+    "    plt.xlim(-5, 5)\n",
+    "    plt.show()\n",
+    "\n",
+    "\n",
+    "widgets.interactive(update_rib_plot, x=field_slider)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a0338f58-bdce-4534-af71-e958156c7fdc",
+   "metadata": {},
+   "source": [
+    "## Optical Mode Simulations\n",
+    "\n",
+    "Using the temperature data of the steady-state heat simulations, we perturb the optical properties of the mediums in preparation for our optical simulations."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "b6aeff8f-2fed-4549-8988-b8936b4af7a1",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "wg_perturbed_scene = wg_scene.perturbed_mediums_copy(temperature=wg_data[\"temperature\"].temperature)\n",
+    "\n",
+    "rib_perturbed_scene = rib_scene.perturbed_mediums_copy(\n",
+    "    temperature=rib_data[\"temperature\"].temperature\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4e0a5749-355a-4c53-a10d-5ea6701b04f7",
+   "metadata": {},
+   "source": [
+    "For both waveguide structures, we will create a list of structures with only the perturbed optical mediums of the scene."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "fc41d174-5701-45f9-91bc-e562c99b65c0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_optical_structures(scene):\n",
+    "    optical_structures = []\n",
+    "    for structure in scene.structures:\n",
+    "        optical_medium = structure.medium.optical\n",
+    "        if optical_medium is None:\n",
+    "            optical_medium = scene.medium.optical\n",
+    "        optical_structures.append(structure.updated_copy(medium=optical_medium))\n",
+    "    return optical_structures"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "422f090a-1218-43ba-932f-43d252e0bca4",
+   "metadata": {},
+   "source": [
+    "Now we create optical simulations using the perturbed optical mediums."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "0d7a96ef-7225-4bcd-98fa-830005a22ad7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "optical_sim_size = (8, 0, 4)\n",
+    "optical_sim_center = (0, 0, 1)\n",
+    "\n",
+    "wg_optic = td.Simulation(\n",
+    "    center=optical_sim_center,\n",
+    "    size=optical_sim_size,\n",
+    "    structures=get_optical_structures(wg_perturbed_scene),\n",
+    "    run_time=1e-15,\n",
+    "    grid_spec=td.GridSpec.auto(min_steps_per_wvl=50, wavelength=wvl0),\n",
+    "    medium=SiO2_perturb.perturbed_copy(temperature=wg_data[\"temperature\"].temperature),\n",
+    ")\n",
+    "\n",
+    "rib_optic = wg_optic.updated_copy(\n",
+    "    structures=get_optical_structures(rib_perturbed_scene),\n",
+    "    medium=SiO2_perturb.perturbed_copy(temperature=rib_data[\"temperature\"].temperature),\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "648697cb-8f1d-43fa-be22-61fba0517677",
+   "metadata": {},
+   "source": [
+    "We now construct the mode solving simulations for the perturbed mediums."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "c952be4f-52d7-499c-b7a2-45f2316461c2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from tidy3d.plugins.mode import ModeSolver\n",
+    "\n",
+    "mode_solver_plane = td.Box.from_bounds(rmin=(-5, 0, -1.5), rmax=(5, 0, 2))\n",
+    "mode_spec = td.ModeSpec(num_modes=1, precision=\"double\")\n",
+    "\n",
+    "wg_mode_solver = ModeSolver(\n",
+    "    simulation=wg_optic, plane=mode_solver_plane, mode_spec=mode_spec, freqs=[freq0]\n",
+    ")\n",
+    "\n",
+    "rib_mode_solver = ModeSolver(\n",
+    "    simulation=rib_optic, plane=mode_solver_plane, mode_spec=mode_spec, freqs=[freq0]\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "eccfe08a-32dd-4be0-a02c-4db70b9d3105",
+   "metadata": {},
+   "source": [
+    "We now run the mode solvers."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "9db08948-3a4e-44c6-bc47-02ee6f2f8cf1",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">18:26:04 EDT </span>Created task <span style=\"color: #008000; text-decoration-color: #008000\">'wg mode'</span> with resource_id                            \n",
+       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'mo-33483943-4b61-4565-bf99-ef9d2b364d7e'</span> and task_type            \n",
+       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'MODE_SOLVER'</span>.                                                     \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m18:26:04 EDT\u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'wg mode'\u001b[0m with resource_id                            \n",
+       "\u001b[2;36m             \u001b[0m\u001b[32m'mo-33483943-4b61-4565-bf99-ef9d2b364d7e'\u001b[0m and task_type            \n",
+       "\u001b[2;36m             \u001b[0m\u001b[32m'MODE_SOLVER'\u001b[0m.                                                     \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>View task using web UI at                                          \n",
+       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=mo-33483943-4b61-4565-bf99-ef9d2b364d7e\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'https://tidy3d.simulation.cloud/workbench?taskId=mo-33483943-4b61-</span></a>\n",
+       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=mo-33483943-4b61-4565-bf99-ef9d2b364d7e\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">4565-bf99-ef9d2b364d7e'</span></a>.                                           \n",
+       "</pre>\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=781813;https://tidy3d.simulation.cloud/workbench?taskId=mo-33483943-4b61-4565-bf99-ef9d2b364d7e\u001b\\\u001b[32m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=309181;https://tidy3d.simulation.cloud/workbench?taskId=mo-33483943-4b61-4565-bf99-ef9d2b364d7e\u001b\\\u001b[32mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=781813;https://tidy3d.simulation.cloud/workbench?taskId=mo-33483943-4b61-4565-bf99-ef9d2b364d7e\u001b\\\u001b[32m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=93372;https://tidy3d.simulation.cloud/workbench?taskId=mo-33483943-4b61-4565-bf99-ef9d2b364d7e\u001b\\\u001b[32mmo\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=781813;https://tidy3d.simulation.cloud/workbench?taskId=mo-33483943-4b61-4565-bf99-ef9d2b364d7e\u001b\\\u001b[32m-33483943-4b61-\u001b[0m\u001b]8;;\u001b\\\n",
+       "\u001b[2;36m             \u001b[0m\u001b]8;id=781813;https://tidy3d.simulation.cloud/workbench?taskId=mo-33483943-4b61-4565-bf99-ef9d2b364d7e\u001b\\\u001b[32m4565-bf99-ef9d2b364d7e'\u001b[0m\u001b]8;;\u001b\\.                                           \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Task folder: <a href=\"https://tidy3d.simulation.cloud/folders/folder-86acd7be-dbf5-477e-9c86-3e20787acc03\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'default'</span></a>.                                            \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mTask folder: \u001b]8;id=559993;https://tidy3d.simulation.cloud/folders/folder-86acd7be-dbf5-477e-9c86-3e20787acc03\u001b\\\u001b[32m'default'\u001b[0m\u001b]8;;\u001b\\.                                            \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "075c9a3cebae49988352c2814417c80d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">18:26:05 EDT </span>Estimated FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.007</span>. Minimum cost depends on task     \n",
+       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>execution details. Use <span style=\"color: #008000; text-decoration-color: #008000\">'web.real_cost(task_id)'</span> to get the billed  \n",
+       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>FlexCredit cost after a simulation run.                            \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m18:26:05 EDT\u001b[0m\u001b[2;36m \u001b[0mEstimated FlexCredit cost: \u001b[1;36m0.007\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": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">18:26:06 EDT </span>status = success                                                   \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m18:26:06 EDT\u001b[0m\u001b[2;36m \u001b[0mstatus = success                                                   \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "48151074f4b346efa3358a96e6465cd3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">18:26:08 EDT </span>loading simulation from simulation_data.hdf5                       \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m18:26:08 EDT\u001b[0m\u001b[2;36m \u001b[0mloading simulation from simulation_data.hdf5                       \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Created task <span style=\"color: #008000; text-decoration-color: #008000\">'rib mode'</span> with resource_id                           \n",
+       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'mo-8c498b17-8ac5-46cd-83e6-08c772c9c37f'</span> and task_type            \n",
+       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'MODE_SOLVER'</span>.                                                     \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'rib mode'\u001b[0m with resource_id                           \n",
+       "\u001b[2;36m             \u001b[0m\u001b[32m'mo-8c498b17-8ac5-46cd-83e6-08c772c9c37f'\u001b[0m and task_type            \n",
+       "\u001b[2;36m             \u001b[0m\u001b[32m'MODE_SOLVER'\u001b[0m.                                                     \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>View task using web UI at                                          \n",
+       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=mo-8c498b17-8ac5-46cd-83e6-08c772c9c37f\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'https://tidy3d.simulation.cloud/workbench?taskId=mo-8c498b17-8ac5-</span></a>\n",
+       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=mo-8c498b17-8ac5-46cd-83e6-08c772c9c37f\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">46cd-83e6-08c772c9c37f'</span></a>.                                           \n",
+       "</pre>\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=577955;https://tidy3d.simulation.cloud/workbench?taskId=mo-8c498b17-8ac5-46cd-83e6-08c772c9c37f\u001b\\\u001b[32m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=923698;https://tidy3d.simulation.cloud/workbench?taskId=mo-8c498b17-8ac5-46cd-83e6-08c772c9c37f\u001b\\\u001b[32mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=577955;https://tidy3d.simulation.cloud/workbench?taskId=mo-8c498b17-8ac5-46cd-83e6-08c772c9c37f\u001b\\\u001b[32m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=270579;https://tidy3d.simulation.cloud/workbench?taskId=mo-8c498b17-8ac5-46cd-83e6-08c772c9c37f\u001b\\\u001b[32mmo\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=577955;https://tidy3d.simulation.cloud/workbench?taskId=mo-8c498b17-8ac5-46cd-83e6-08c772c9c37f\u001b\\\u001b[32m-8c498b17-8ac5-\u001b[0m\u001b]8;;\u001b\\\n",
+       "\u001b[2;36m             \u001b[0m\u001b]8;id=577955;https://tidy3d.simulation.cloud/workbench?taskId=mo-8c498b17-8ac5-46cd-83e6-08c772c9c37f\u001b\\\u001b[32m46cd-83e6-08c772c9c37f'\u001b[0m\u001b]8;;\u001b\\.                                           \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Task folder: <a href=\"https://tidy3d.simulation.cloud/folders/folder-86acd7be-dbf5-477e-9c86-3e20787acc03\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'default'</span></a>.                                            \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mTask folder: \u001b]8;id=591395;https://tidy3d.simulation.cloud/folders/folder-86acd7be-dbf5-477e-9c86-3e20787acc03\u001b\\\u001b[32m'default'\u001b[0m\u001b]8;;\u001b\\.                                            \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "65e0f93398f34cbb92958c54602b88e4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">18:26:10 EDT </span>Estimated FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.005</span>. Minimum cost depends on task     \n",
+       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>execution details. Use <span style=\"color: #008000; text-decoration-color: #008000\">'web.real_cost(task_id)'</span> to get the billed  \n",
+       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>FlexCredit cost after a simulation run.                            \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m18:26:10 EDT\u001b[0m\u001b[2;36m \u001b[0mEstimated FlexCredit cost: \u001b[1;36m0.005\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": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">18:26:11 EDT </span>status = success                                                   \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m18:26:11 EDT\u001b[0m\u001b[2;36m \u001b[0mstatus = success                                                   \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bed719acc8b548fc9d0746f92942e5d6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">18:26:14 EDT </span>loading simulation from simulation_data.hdf5                       \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m18:26:14 EDT\u001b[0m\u001b[2;36m \u001b[0mloading simulation from simulation_data.hdf5                       \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "wg_mode_data = web.run(wg_mode_solver, task_name=\"wg mode\", reduce_simulation=True)\n",
+    "rib_mode_data = web.run(rib_mode_solver, task_name=\"rib mode\", reduce_simulation=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f9ef67b6-cc74-4216-ae75-fbf75bd008b9",
+   "metadata": {},
+   "source": [
+    "We plot the mode field profiles to ensure the mode solving was done correctly."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "0d1298ca-a245-468c-91dc-66901cbcbd37",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1500x500 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(1, 2, figsize=(15, 5))\n",
+    "\n",
+    "wg_mode_solver.plot_field(field_name=\"E\", val=\"abs\", ax=ax[0])\n",
+    "rib_mode_solver.plot_field(field_name=\"E\", val=\"abs\", ax=ax[1])\n",
+    "ax[0].set_xlim(-2, 2)\n",
+    "ax[0].set_ylim(-0.5, 0.8)\n",
+    "ax[1].set_xlim(-2, 2)\n",
+    "ax[1].set_ylim(-0.5, 0.8)\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "62ded028-d006-4cfe-91c0-13afac8674ce",
+   "metadata": {},
+   "source": [
+    "Finally we use the computed mode data to find the loss:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "2e96f5d6-ae16-4ea0-9043-297728e44e44",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Attenuation: 0.0 dB/cm for the strip waveguide and 0.315 dB/cm for the rib waveguide.\n"
+     ]
+    }
+   ],
+   "source": [
+    "alpha_wg = wg_mode_data.modes_info[\"loss (dB/cm)\"].item()\n",
+    "alpha_rib = rib_mode_data.modes_info[\"loss (dB/cm)\"].item()\n",
+    "\n",
+    "print(\n",
+    "    f\"Attenuation: {alpha_wg} dB/cm for the strip waveguide and {round(alpha_rib, 3)} dB/cm for the rib waveguide.\"\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "applications": [
+   "Active photonic integrated circuit components",
+   "Nanophotonics",
+   "Heat"
+  ],
+  "description": "This example demonstrates the usage of the Tidy3D's steady and unsteady heat analyses to compare the performances of thermo-optic phase-shifters based on TiN metal and N++ doped silicon in the silicon-on-insulator (SOI) platform.",
+  "feature_image": "./img/transient_thermooptic.png",
+  "features": [
+   "Heat"
+  ],
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "keywords": "silicon, SOI, Tidy3D, heat",
+  "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": "Transient heat analysis of thermo-optic phase-shifters"
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/case_studies/pic_active.rst b/docs/case_studies/pic_active.rst
index e540ab90..508c5a16 100644
--- a/docs/case_studies/pic_active.rst
+++ b/docs/case_studies/pic_active.rst
@@ -13,3 +13,4 @@ At the moment, Tidy3D’s heat solver can be used with the FDTD solver to model
     ../../MachZehnderModulator
     ../../ThermoOpticDopedModulator
     ../../HeatDissipationSOI
+    ../../TransientThermoOpticShifter
diff --git a/docs/features/heat.rst b/docs/features/heat.rst
index ce6d4bbb..0e2c9b19 100644
--- a/docs/features/heat.rst
+++ b/docs/features/heat.rst
@@ -8,4 +8,5 @@ This section introduces the HEAT solver, a Tidy3D feature that solves the steady
     :maxdepth: 1
 
     ../../HeatSolver
-    ../../CPOHeat
\ No newline at end of file
+    ../../CPOHeat
+    ../../TransientThermoOpticShifter
\ No newline at end of file
diff --git a/img/transient_thermooptic.png b/img/transient_thermooptic.png
new file mode 100644
index 00000000..222d1e0c
Binary files /dev/null and b/img/transient_thermooptic.png differ