ChargeSolver.ipynb

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    "# Charge solver: a silicon electro-optic phase modulator\n",
    "\n",
    "In this notebook, we simulate a silicon electro-optic phase modulator. The numerical results are compared with the experimental data provided in [1]. The modulator consists of a *pn*-junction with a high doping contact to allow for a less resistive electrical interface. \n",
    "\n",
    "The sketch below illustrates the main dimensions of the modulator:\n",
    "\n",
    "![charge_modulator](img/charge_modulator.png)\n",
    "\n",
    "### Outline\n",
    "\n",
    "1. We start by setting up a `Charge` simulation in `tidy3d`. For this example, the isothermal Drift-Diffusion (DD) equations are solved. A basic summary of the equations solved and nomenclature used can be found our [td.SemiconductorMedium](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.SemiconductorMedium.html#tidy3d-semiconductormedium) documentation. \n",
    "2. We show how the numerical results from this simulation can be used to determine the optical phase change.\n",
    "\n",
    "### References\n",
    "\n",
    "[1] Chrostowski L, Hochberg M. Silicon Photonics Design: From Devices to Systems. Cambridge University Press; 2015.\n",
    "\n",
    "[2] Schroeder, D., T. Ostermann, and O. Kalz. “Comparison of transport models far the simulation of degenerate semiconductors.” Semiconductor science and technology 9.4 (1994): 364.\n"
   ]
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   "source": [
    "## 1. Construct our `Charge` Simulation"
   ]
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   "source": [
    "import numpy as np\n",
    "import tidy3d as td\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
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   "source": [
    "### Specify our Modulator Dimensions"
   ]
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    "Based on the figure above, we can define the dimensions of our electro-optic phase modulator. These are defaulted to micro-meters.\n",
    "\n",
    "Since 2D problems are not yet supported, we will extrude our geometry in the *z* dimension. This is perpendicular to the plane of the problem in `z_size` $\\mu m$."
   ]
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   "source": [
    "# all units in um\n",
    "w_core = 0.25\n",
    "h_core = 0.22\n",
    "\n",
    "w_clearance = 2.25\n",
    "h_clearance = 0.09\n",
    "w_side = 2.5\n",
    "h_side = 0.22\n",
    "\n",
    "w_contact = 1.2\n",
    "h_contact = 1.\n",
    "\n",
    "z_size = h_clearance/5\n",
    "\n",
    "res = h_clearance/10"
   ]
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    "### Create Multi-Physical Mediums\n",
    "\n",
    "In this example, we want to simulate the optical and electronic behaviour of this device. This means we need to specify material models that can represent the behaviour in these two physical domains. \n",
    "\n",
    "The [td.MultiPhysicsMedium](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.components.material.multi_physics.MultiPhysicsMedium.html#tidy3d.components.material.multi_physics.MultiPhysicsMedium) was introduced in order to compose existing `tidy3d` medium models together into a unified multi-domain representation. `td.MultiPhysics` medium will be fully interoperable with any `tidy3d` simulation definition or class that uses existing medium definitions, and should enable full flexibility in terms of creating unified material models progressively. In this example, we will focus on its usage for Charge simulations which it fully supports.\n",
    "\n",
    "We still need to define a `<Domain>Medium` model for our Charge and Optical FDTD simulations. Let's explore the different ways of doing this below."
   ]
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    "Our silica ($SiO_2$) cladding behaves like an electronic insulator which can be defined with a [td.ChargeInsulatorMedium](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.ChargeInsulatorMedium.html#tidy3d.ChargeInsulatorMedium). For the optical simulation, assuming we have an `td.Medium` frequency range valid to 0 Hz (electronic DC), we can reuse a the [td.material_library](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/material_library.html) optical medium models and create a `td.MultiPhysicsMedium` accordingly:"
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       "<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\">11:31:24 CET </span><span style=\"color: #800000; text-decoration-color: #800000\">WARNING: frequency passed to </span><span style=\"color: #008000; text-decoration-color: #008000\">'Medium.eps_model()'</span><span style=\"color: #800000; text-decoration-color: #800000\">is outside of     </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'Medium.frequency_range'</span><span style=\"color: #800000; text-decoration-color: #800000\"> = </span><span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">59958491600000.0</span><span style=\"color: #800000; text-decoration-color: #800000\">, </span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1998616386666666.8</span><span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">)</span><span style=\"color: #800000; text-decoration-color: #800000\">  </span>\n",
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       "\u001b[2;36m11:31:24 CET\u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING: frequency passed to \u001b[0m\u001b[32m'Medium.eps_model\u001b[0m\u001b[32m(\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m\u001b[31mis outside of     \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[32m'Medium.frequency_range'\u001b[0m\u001b[31m = \u001b[0m\u001b[1;31m(\u001b[0m\u001b[1;36m59958491600000.0\u001b[0m\u001b[31m, \u001b[0m\u001b[1;36m1998616386666666.8\u001b[0m\u001b[1;31m)\u001b[0m\u001b[31m  \u001b[0m\n"
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     "text": [
      "/home/momchil/miniconda3/envs/flex/lib/python3.12/site-packages/pydantic/v1/validators.py:158: ComplexWarning: Casting complex values to real discards the imaginary part\n",
      "  return float(v)\n"
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   "source": [
    "SiO2_optic = td.material_library['SiO2']['Palik_Lossless']\n",
    "SiO2 = td.MultiPhysicsMedium(\n",
    "    optical=SiO2_optic,\n",
    "    charge=td.ChargeInsulatorMedium(permittivity=SiO2_optic.eps_model(frequency=0)), \n",
    "    name=\"SiO2\",\n",
    ")"
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   "source": [
    "However, in this example, we will create our own silica multi-physics model. The above `td.material_library['SiO2']['Palik_Lossless']` is not well defined for `frequency=0`. Hence, it is outside of the simulation validity region. \n",
    "<!-- Also, the mode solver doesn't yet support Multiphysics mediums. TODO remove Marc is this relevant for this simulation?-->"
   ]
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   "id": "1c5efbbc-03b7-4906-a6d5-27844161a9c0",
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   "source": [
    "SiO2 = td.MultiPhysicsMedium(\n",
    "    optical=td.Medium(permittivity=3.9),\n",
    "    charge=td.ChargeInsulatorMedium(permittivity=3.9), # redefining permittivity\n",
    "    name=\"SiO2\",\n",
    ")"
   ]
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   "source": [
    "And now we define the remaining auxiliary mediums used to define our boundary conditions:"
   ]
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    "aux = td.MultiPhysicsMedium(\n",
    "    charge=td.ChargeConductorMedium(conductivity=1),\n",
    "    name=\"aux\"\n",
    ")\n",
    "\n",
    "air = td.MultiPhysicsMedium(\n",
    "    heat=td.FluidSpec(),\n",
    "    name=\"air\"\n",
    ")"
   ]
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    "### A Drift-Diffusion Model defined in a `td.SemiconductorMedium`\n",
    "\n",
    "We will begin defining the electronic properties of the intrinsic semiconductor: i.e., the [td.SemiconductorMedium](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.SemiconductorMedium.html) without any doping. \n",
    "\n",
    "Within a `SemiconductorMedium`, the Drift-Diffusion (DD) equations will be solved. In this example, we will explore an isothermal (constant temperature) case. This simulation performed at $ T=300 $ K. The DD equations, their assumptions, and limitations for this type of problem are discussed below:\n",
    "\n",
    "The electrostatic potential $\\psi$, the electron $n$ and hole $p$ concentrations are computed from the following coupled system of PDEs.\n",
    "\n",
    "$$\n",
    "-\\nabla \\cdot \\left( \\varepsilon_0 \\varepsilon_r \\nabla \\psi \\right) = q \\left( p - n + N_d^+ - N_a^- \\right)\n",
    "$$\n",
    "\n",
    "$$\n",
    "q \\frac{\\partial n}{\\partial t} = \\nabla \\cdot \\mathbf{J_n} - qR\n",
    "$$\n",
    "\n",
    "$$\n",
    "q \\frac{\\partial p}{\\partial t} = -\\nabla \\cdot \\mathbf{J_p} - qR\n",
    "$$\n",
    "\n",
    "The above system requires the definition of the generation-recombination rate $R$ represented by our [generation-recombination](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/charge/index.html#generation-recombination) models and the flux functions (free carrier current density), $ \\mathbf{J_n} $ and $ \\mathbf{J_p} $. Their usual form is:\n",
    "\n",
    "$$\n",
    "\\mathbf{J_n} = q \\mu_n \\mathbf{F_{n}} + q D_n \\nabla n\n",
    "$$\n",
    "\n",
    "$$\n",
    "\\mathbf{J_p} = q \\mu_p \\mathbf{F_{p}} - q D_p \\nabla p\n",
    "$$\n",
    "\n",
    "These depend on the carrier mobility for electrons $\\mu_n$ and holes $\\mu_p$ represented by our [mobility models](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/charge/index.html#mobility).\n",
    "\n",
    "Where the effective field, defined in [2], is simplified to:\n",
    "\n",
    "$$\n",
    "\\mathbf{F_{n,p}} = \\nabla \\psi\n",
    "$$\n",
    "\n",
    "This approximation does not consider the effects of bandgap narrowing and degeneracy on the effective electric field $ \\mathbf{F_{n,p}} $, which is acceptable for non-degenerate semiconductors. [Bandgap narrowing models](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/charge/index.html#bandgap) which are important for doped semiconductor effects can also be defined.\n",
    "\n",
    "Material properties are defined as class parameters or other classes:\n",
    "\n",
    "| Symbol       | Parameter Name          | Description                                   |\n",
    "|--------------|-------------------------|-----------------------------------------------|\n",
    "| $$ N_a $$    | `N_a`                  | Ionized acceptors density                     |\n",
    "| $$ N_d $$    | `N_d`                  | Ionized donors density                        |\n",
    "| $$ N_c $$    | `N_c`                  | Effective density of states in the conduction band |\n",
    "| $$ N_v $$    | `N_v`                  | Effective density of states in valence band   |\n",
    "| $$ R $$      | `R`                    | Generation-Recombination term                |\n",
    "| $$ E_g $$    | `E_g`                  | Bandgap Energy                                |\n",
    "| $$ \\Delta E_g $$ | `delta_E_g`         | Bandgap Narrowing                             |\n",
    "| $$ \\sigma $$ | `conductivity`         | Electrical conductivity                       |\n",
    "| $$ \\varepsilon_r $$ | `permittivity`  | Relative permittivity                         |\n",
    "| $$ \\varepsilon_0 $$ | `tidy3d.constants.EPSILON_0`  | Free Space permittivity                         |\n",
    "| $$ q $$      | `tidy3d.constants.Q_e` | Fundamental electron charge                   |\n",
    "\n"
   ]
  },
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   "source": [
    "Our material library already has a silicon `td.MultiPhysicsMedium` model valid for a 300K isothermal case which incorporates a `td.SemiconductorMedium`:"
   ]
  },
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   "source": [
    "intrinsic_si = td.material_library['cSi'].variants['Si_MultiPhysics'].medium.charge"
   ]
  },
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   "source": [
    "In case we want to build our own `td.SemiconductorMedium`, we can do so in the following manner (note that is identical to the one above):"
   ]
  },
  {
   "cell_type": "code",
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   "id": "61d83e4f-2622-45ba-851c-6ddebf382156",
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    "# Create a semiconductor medium with mobility, generation-recombination, and bandgap narrowing models.\n",
    "intrinsic_si = td.SemiconductorMedium(\n",
    "    permittivity=11.7,\n",
    "    N_c=2.86e19,\n",
    "    N_v=3.1e19,\n",
    "    E_g=1.11,\n",
    "    mobility_n=td.CaugheyThomasMobility(\n",
    "        mu_min=52.2,\n",
    "        mu=1471.0,\n",
    "        ref_N=9.68e16,\n",
    "        exp_N=0.68,\n",
    "        exp_1=-0.57,\n",
    "        exp_2=-2.33,\n",
    "        exp_3=2.4,\n",
    "        exp_4=-0.146,\n",
    "    ),\n",
    "    mobility_p=td.CaugheyThomasMobility(\n",
    "        mu_min=44.9,\n",
    "        mu=470.5,\n",
    "        ref_N=2.23e17,\n",
    "        exp_N=0.719,\n",
    "        exp_1=-0.57,\n",
    "        exp_2=-2.33,\n",
    "        exp_3=2.4,\n",
    "        exp_4=-0.146,\n",
    "    ),\n",
    "    R=[\n",
    "        td.ShockleyReedHallRecombination(tau_n=3.3e-6, tau_p=4e-6),\n",
    "        td.RadiativeRecombination(r_const=1.6e-14),\n",
    "        td.AugerRecombination(c_n=2.8e-31, c_p=9.9e-32),\n",
    "    ],\n",
    "    delta_E_g=td.SlotboomBandGapNarrowing(\n",
    "        v1=6.92 * 1e-3,\n",
    "        n2=1.3e17,\n",
    "        c2=0.5,\n",
    "        min_N=1e15,\n",
    "    ),\n",
    "    N_a=0,\n",
    "    N_d=0,\n",
    ")"
   ]
  },
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   "source": [
    "### Create our doping regions"
   ]
  },
  {
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    "In our intrinsic silicon model above, we set the ionized acceptors $N_a$ and ionized donor $N_d$ density to zero. Since we will be [doping](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/charge/index.html#doping) our semiconductors with acceptors and donors, we need to create these doping distributions. \n",
    "\n",
    "In this examples, we'll use Gaussian doping boxes using the [td.GaussianDoping](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.GaussianDoping.html#tidy3d.GaussianDoping) model. The Gaussian doping concentration $N$ is defined in the following manner:\n",
    "\n",
    "- $N = N_{\\text{max}}$ at locations more than `width` $\\mu m$ away from the sides of the box.\n",
    "- $N = N_{\\text{ref}}$ at locations on the box sides.\n",
    "- A Gaussian variation between $N_{\\text{max}}$ and $N_{\\text{ref}}$ at locations less than `width` µm away from the sides.\n",
    "\n",
    "By definition, all sides of the box will have a concentration $N_{\\text{ref}}$ (except the side specified as the source), and the center of the box (`width` away from the box sides) will have a concentration $N_{\\text{max}}$.\n",
    "\n",
    "$$\n",
    "N = \\{N_{\\text{max}}\\} \\exp \\left[\n",
    "- \\ln \\left( \\frac{\\{N_{\\text{max}}\\}}{\\{N_{\\text{ref}}\\}} \\right)\n",
    "\\left( \\frac{(x|y|z) - \\{(x|y|z)_{\\text{box}}\\}}{\\text{width}} \\right)^2\n",
    "\\right]\n",
    "$$\n"
   ]
  },
  {
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   "execution_count": 8,
   "id": "7185663c-8386-400e-ae2c-a0f848be12db",
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   "source": [
    "# doping with boxes\n",
    "acceptor_boxes = []\n",
    "donor_boxes = []\n",
    "\n",
    "acceptor_boxes.append(\n",
    "    td.ConstantDoping.from_bounds(\n",
    "        rmin=[-5, 0, -np.inf],\n",
    "        rmax=[5, 0.22, np.inf],\n",
    "        concentration=1e15\n",
    "    )\n",
    ")\n",
    "\n",
    "# p implant\n",
    "acceptor_boxes.append(\n",
    "    td.GaussianDoping.from_bounds(\n",
    "    rmin=[-6, -0.3, -np.inf],\n",
    "    rmax=[-0.15, 0.098, np.inf], \n",
    "    concentration=7e17,\n",
    "    ref_con=1e6,\n",
    "    width=0.1,\n",
    "    source=\"ymax\")\n",
    ")\n",
    "\n",
    "# n implant\n",
    "donor_boxes.append(\n",
    "    td.GaussianDoping.from_bounds(\n",
    "        rmin=[0.15, -0.3, -np.inf],\n",
    "        rmax=[6, 0.098, np.inf], \n",
    "        concentration=5e17,\n",
    "        ref_con=1e6,\n",
    "        width=0.1,\n",
    "        source=\"ymax\"\n",
    "    )\n",
    ")\n",
    "\n",
    "# p++\n",
    "acceptor_boxes.append(\n",
    "    td.GaussianDoping.from_bounds(\n",
    "        rmin=[-6, -0.3, -np.inf],\n",
    "        rmax=[-2, 0.22, np.inf], \n",
    "        concentration=1e19,\n",
    "        ref_con=1e6,\n",
    "        width=0.1,\n",
    "        source=\"ymax\"\n",
    "    )\n",
    ")\n",
    "\n",
    "# # n++\n",
    "donor_boxes.append(\n",
    "    td.GaussianDoping.from_bounds(\n",
    "        rmin=[2, -0.3, -np.inf],\n",
    "        rmax=[6, 0.22, np.inf], \n",
    "        concentration=1e19,\n",
    "        ref_con=1e6,\n",
    "        width=0.1,\n",
    "        source=\"ymax\")\n",
    ")\n",
    "\n",
    "\n",
    "# p wg implant\n",
    "acceptor_boxes.append(\n",
    "    td.GaussianDoping.from_bounds(\n",
    "        rmin=[-0.3, 0, -np.inf],\n",
    "        rmax=[0.06, 0.255, np.inf],\n",
    "        concentration=5e17,\n",
    "        ref_con=1e6,\n",
    "        width=0.12,\n",
    "        source=\"xmin\"\n",
    "    )\n",
    ")\n",
    "\n",
    "# n wg implant\n",
    "donor_boxes.append(\n",
    "    td.GaussianDoping.from_bounds(\n",
    "        rmin=[-0.06, 0.02, -np.inf],\n",
    "        rmax=[0.25, 0.26, np.inf], \n",
    "        concentration=7e17,\n",
    "        ref_con=1e6,\n",
    "        width=0.11,\n",
    "        source=\"xmax\")\n",
    ")"
   ]
  },
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   "id": "9d38d4a8-b212-472a-abdf-8a06b68c9ff5",
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   "source": [
    "Once we have our doping distributions, we can add it to our `td.SemiconductorMedium` and create our `MultiPhysicsMedium` to create our device structure with it. \n",
    "To include the doping to our semiconductor properties, we can implement this by creating a copy of it through the function `updated_copy()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5c66912e-60a0-4ac6-84e1-f47a3dc2e4f4",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "Si_2D_doping = td.MultiPhysicsMedium(\n",
    "    charge=intrinsic_si.updated_copy(\n",
    "        N_d=donor_boxes,\n",
    "        N_a=acceptor_boxes,\n",
    "    ),\n",
    "    name=\"Si_doping\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eebd4848-0475-4b2d-b7d4-fc0f889b641c",
   "metadata": {},
   "source": [
    "### Compose our device structures\n",
    "\n",
    "We can now combine our medium definitions and geometry models to create our device structures."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f6f3c02e-341f-453a-901a-4c9beb7c92d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# create objects\n",
    "overlap_factor = 1.0001\n",
    "\n",
    "oxide = td.Structure(\n",
    "    geometry=td.Box(center=(0, h_core, 0), size=(10, 5, td.inf)),\n",
    "    medium=SiO2,\n",
    "    name=\"oxide\"\n",
    ")\n",
    "\n",
    "core_p = td.Structure(\n",
    "    geometry=td.Box(center=(-w_core/2, h_core/2, 0), size=(w_core, h_core, td.inf)),\n",
    "    medium=Si_2D_doping,\n",
    "    name=\"core_p\"\n",
    ")\n",
    "\n",
    "core_n = td.Structure(\n",
    "    geometry=td.Box(center=(w_core/2, h_core/2, 0), size=(w_core, h_core, td.inf)),\n",
    "    medium=Si_2D_doping,\n",
    "    name=\"core_n\"\n",
    ")\n",
    "\n",
    "clearance_p = td.Structure(\n",
    "    geometry=td.Box(center=(-w_core - w_clearance/2, h_clearance/2, 0), size=(w_clearance, h_clearance, td.inf)),\n",
    "    medium=Si_2D_doping,\n",
    "    name=\"clearance_p\"\n",
    ")\n",
    "\n",
    "clearance_n = td.Structure(\n",
    "    geometry=td.Box(center=(w_core + w_clearance/2, h_clearance/2, 0), size=(w_clearance, h_clearance, td.inf)),\n",
    "    medium=Si_2D_doping,\n",
    "    name=\"clearance_n\"\n",
    ")\n",
    "\n",
    "side_p = td.Structure(\n",
    "    geometry=td.Box(center=(-w_core - w_clearance - w_side/2, h_side/2, 0), size=(w_side, h_side, td.inf)),\n",
    "    medium=Si_2D_doping,\n",
    "    name=\"side_p\"\n",
    ")\n",
    "\n",
    "side_n = td.Structure(\n",
    "    geometry=td.Box(center=(w_core + w_clearance + w_side/2, h_side/2, 0), size=(w_side, h_side, td.inf)),\n",
    "    medium=Si_2D_doping,\n",
    "    name=\"side_n\"\n",
    ")\n",
    "\n",
    "# create a couple structs to define the contacts\n",
    "contact_p = td.Structure(\n",
    "    geometry=td.Box(center=(-w_core - w_clearance - w_side + w_contact/2, h_side + h_contact/2, 0), size=(w_contact, h_contact, td.inf)),\n",
    "    medium=aux,\n",
    "    name=\"contact_p\"\n",
    ")\n",
    "\n",
    "contact_n = td.Structure(\n",
    "    geometry=td.Box(center=(w_core + w_clearance + w_side - w_contact/2, h_side + h_contact/2, 0), size=(w_contact, h_contact, td.inf)),\n",
    "    medium=aux,\n",
    "    name=\"contact_n\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7a43d65-40df-401b-8615-3cdcb48315dc",
   "metadata": {},
   "source": [
    "A `td.Scene` helps us visualise that the structures are correctly positioned in this simulation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "acab3c3b-f815-4afa-83c8-b486c466817f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create a scene with the previous structures\n",
    "all_structures = [oxide, core_p, core_n, clearance_n, clearance_p, side_p, side_n, contact_p, contact_n]\n",
    "\n",
    "scene = td.Scene(\n",
    "    medium=air,\n",
    "    structures=all_structures,\n",
    ")\n",
    "\n",
    "\n",
    "_, ax = plt.subplots(2, 1, figsize=(6, 6))\n",
    "\n",
    "scene.plot(z=0, ax=ax[0])\n",
    "scene.plot(y=h_core/2, ax=ax[1])\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e922e67-7ec7-4439-b415-9266a9c6f73c",
   "metadata": {},
   "source": [
    "And to make sure our doping concentration regions are correct, we can visualize them with the convencience function [`plot_structures_property`](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.Scene.html#tidy3d.Scene.plot_structures_property). \n",
    "\n",
    "In the plots below, we have used `property=\"doping\"`, but we can also use `property=\"acceptors\"` or `property=\"donors\"` to check individual doping concentration regions. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ccdc85c2-1583-4dba-9a1c-e9c6447a3829",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: title={'center': 'cross section at z=0.00 (μm)'}, xlabel='x (μm)', ylabel='y (μm)'>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x400 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot doping from scene\n",
    "_, ax = plt.subplots(1,2, figsize=(15, 4))\n",
    "scene.plot_structures_property(z=0, property=\"doping\", ax=ax[0], limits=[-1e18,1e18])\n",
    "scene.plot_structures_property(z=0, property=\"doping\", ax=ax[1], hlim=[-0.5,0.5], vlim=[-0.5,0.5], limits=[-5e17,5e17])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "468a3753-b1a2-4da0-b749-1bd12a2f4e8e",
   "metadata": {},
   "source": [
    "### Charge Boundary Conditions\n",
    "\n",
    "In this example, we will apply a DC voltage bias across the modulator junction at different voltages. We can do this by using the `VoltageBC` defined at both the p and n regions of the simulation region. This boundary condition accepts a [`td.DCVoltageSource`](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.DCVoltageSource.html#tidy3d.DCVoltageSource) model. This `DCVoltageSource` accepts a single float or an array of floats. In the latter case, a computation result for each of the voltages defined in the array will be calculated. \n",
    "\n",
    "The modulator will operate in reverse bias. This means a zero voltage is applied to the p side and a scalar positive voltage is applied to the n side."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f2b82982-f8d3-4cb5-b8c1-953460f90d06",
   "metadata": {},
   "outputs": [],
   "source": [
    "# create BCs\n",
    "voltages = [-0.5, 0, 1, 2, 3, 4]\n",
    "\n",
    "bc_v1 = td.HeatChargeBoundarySpec(\n",
    "    condition=td.VoltageBC(source=td.DCVoltageSource(voltage=0)),\n",
    "    placement=td.StructureBoundary(structure=contact_p.name),\n",
    ")\n",
    "\n",
    "bc_v2 = td.HeatChargeBoundarySpec(\n",
    "    condition=td.VoltageBC(source=td.DCVoltageSource(voltage=voltages)),\n",
    "    placement=td.StructureBoundary(structure=contact_n.name),\n",
    ")\n",
    "\n",
    "boundary_conditions = [bc_v1, bc_v2]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "172cf928-a894-477c-a8c0-092b429f2241",
   "metadata": {},
   "source": [
    "### Create our Monitors\n",
    "\n",
    "Monitors are used to determine exactly what data is to be extracted from the computation. In the case of a Charge simulation, we can extract electronic properties such as the potential $\\psi$, free carriers distribution $\\mathbf{J}$, and capacitance $C$.\n",
    "\n",
    "A [td.SteadyCapacitanceMonitor](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.SteadyCapacitanceMonitor.html#tidy3d.SteadyCapacitanceMonitor) is used to monitor small signal capacitance within the area/volume defined by the monitor. \n",
    "\n",
    "The small signal-capacitance of electrons $C_n$ and holes $C_p$ is computed from the charge due to electrons $Q_n$ and holes $Q_p$ at an applied voltage $V$ at a voltage difference $\\Delta V$ between two simulations.\n",
    "\n",
    "$$\n",
    "C_{n,p} = \\frac{Q_{n,p}(V + \\Delta V) - Q_{n,p}(V)}{\\Delta V}\n",
    "$$\n",
    "\n",
    "\n",
    "Note that if only one solution is calculated (one single voltage data point is computed) the output for this monitor will be empty.\n",
    "\n",
    "In order to extract the electrostatic potential $\\psi$ from our simulation, a [td.SteadyPotentialMonitor](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.SteadyPotentialMonitor.html#tidy3d.SteadyPotentialMonitor) results in a [td.SteadyPotentialData](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.SteadyPotentialData.html#tidy3d.SteadyPotentialData).\n",
    "\n",
    "A [td.SteadyFreeCarrierMonitor](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.SteadyFreeCarrierMonitor.html#tidy3d.SteadyFreeCarrierMonitor) let us record the steady-state of the free carrier concentration ($\\mathbf{J_n}$ electrons and $\\mathbf{J_p}$ holes) so that we can visualize results. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f33a4020-c244-4ba2-a785-b15081838ffc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# capacitance monitors\n",
    "capacitance_global_mnt = td.SteadyCapacitanceMonitor(\n",
    "    center=(0, 0.14, 0), size=(td.inf, td.inf, 0), name=\"capacitance_global_mnt\",\n",
    ")\n",
    "\n",
    "# charge monitor around the waveguide\n",
    "capacitance_wvg_mnt = td.SteadyCapacitanceMonitor(\n",
    "    center=(0, 0.14, 0), size=(0.6, 0.3, 0), name=\"capcitance_wvg_mnt\",\n",
    ")\n",
    "\n",
    "# charge monitor around the waveguide\n",
    "charge_3D_mnt = td.SteadyFreeCarrierMonitor(\n",
    "    center=(0, 0.14, 0), size=(0.6, 0.3, td.inf), name=\"charge_3D_mnt\", unstructured=True\n",
    ")\n",
    "\n",
    "charge_monitor_z0 = td.SteadyFreeCarrierMonitor(\n",
    "    center=(0, 0.14, 0), size=(0.6, 0.3, 0), name=\"charge_z0\", unstructured=True, \n",
    ")\n",
    "\n",
    "# voltage monitor around waveguide\n",
    "voltage_monitor_z0 = td.SteadyPotentialMonitor(\n",
    "    center=(0, 0.14, 0), size=(0.6, 0.3, 0), name=\"voltage_z0\", unstructured=True, \n",
    ")\n",
    "\n",
    "# Will be used later for the mode simulations\n",
    "charge_monitor_z0_big = td.SteadyFreeCarrierMonitor(\n",
    "    center=(0, 0.14, 0), size=(td.inf, td.inf, 0), name=\"charge_z0_big\", unstructured=True, conformal=True\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa753195-995e-4c58-89ab-15c9b2e1bfe1",
   "metadata": {},
   "source": [
    "### Mesh and Convergence criteria\n",
    "Before we can actually create the simulation object, we need to define some mesh parameters and convergence/tolerance criteria. One important note here is that in Charge simulations, we need to specify the type of simulation we're interested in running. In this case, we want to run a steady state isothermal analysis which is specified with the class `IsothermalSteadyChargeDCAnalysis`. Note that this class takes an argument `convergence_dv`. To understand how this parameter affects convergence, let's imagine a case where we want to solve for 0 V and 20 V bias. In this case the solver will first try to solve the 0V bias case and based off of this solution will go ahead and try to solve the 20V bias case. Since these are two very different biases, the solver may find it difficult to solve the second. By setting e.g.,`convergence_dv=10` we'll force the solver to go from 0V to 20V in 10V steps which eases convergence.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "7929e468-e117-4e09-b264-b244060999eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# mesh\n",
    "mesh = td.DistanceUnstructuredGrid(dl_interface=res*1.14, dl_bulk=res*4, distance_interface=0.02*h_side,\n",
    "    distance_bulk=1*h_side, relative_min_dl=0,\n",
    "    uniform_grid_mediums=[Si_2D_doping.name],\n",
    "    non_refined_structures=[oxide.name]\n",
    ")\n",
    "\n",
    "# devsim setting \n",
    "convergence_settings = td.ChargeToleranceSpec(rel_tol=1e5, abs_tol=3e3, max_iters=400)\n",
    "\n",
    "# currently we support Isothermal cases only. Temperature in K\n",
    "analysis_type = td.IsothermalSteadyChargeDCAnalysis(temperature=300, convergence_dv=10, tolerance_settings=convergence_settings)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "708f011c-4f21-4a81-aff6-a632d286de89",
   "metadata": {},
   "source": [
    "### Create our `HeatChargeSimulation`\n",
    "We're now ready to create the actual simulation object. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "076ab75a-8f63-49ee-99d5-a458da89d626",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x1500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# build heat simulation object\n",
    "charge_sim = td.HeatChargeSimulation(\n",
    "    sources=[],\n",
    "    monitors=[capacitance_global_mnt, capacitance_wvg_mnt, charge_3D_mnt, charge_monitor_z0, voltage_monitor_z0, charge_monitor_z0_big],\n",
    "    analysis_spec=analysis_type,\n",
    "    center=(0,0,0),\n",
    "    size=(10.5,10,z_size),\n",
    "    structures=all_structures,\n",
    "    medium=air,\n",
    "    boundary_spec=boundary_conditions,\n",
    "    grid_spec=mesh,\n",
    "    symmetry=(0, 0, 0)\n",
    "    )\n",
    "\n",
    "# plot simulation\n",
    "fig, ax = plt.subplots(1, 2, figsize=(10, 15))\n",
    "charge_sim.plot(z=0, ax=ax[0])\n",
    "charge_sim.plot_property(z=0, property=\"electric_conductivity\", ax=ax[1])\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2db0a29f-d7f0-48f2-9b0a-f6570920f06c",
   "metadata": {},
   "source": [
    "### Run Simulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "a8c08b40-e65a-422d-9597-d7308bbfac8f",
   "metadata": {},
   "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\">11:32:34 CET </span>Created task <span style=\"color: #008000; text-decoration-color: #008000\">'charge_junction'</span> with task_id                        \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'hec-a49326a6-6361-4c64-b7fe-b4c0fa3d8894'</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;36m11:32:34 CET\u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'charge_junction'\u001b[0m with task_id                        \n",
       "\u001b[2;36m             \u001b[0m\u001b[32m'hec-a49326a6-6361-4c64-b7fe-b4c0fa3d8894'\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": "e8f30ca387774046927ebfd0cb083503",
       "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\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">11:32:37 CET </span>Maximum 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;36m11:32:37 CET\u001b[0m\u001b[2;36m \u001b[0mMaximum 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\">11:32:38 CET </span>status = queued                                                    \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m11:32:38 CET\u001b[0m\u001b[2;36m \u001b[0mstatus = queued                                                    \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>To cancel the simulation, use <span style=\"color: #008000; text-decoration-color: #008000\">'web.abort(task_id)'</span> or              \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'web.delete(task_id)'</span> or abort/delete the task in the web UI.      \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Terminating the Python script will not stop the job running on the \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>cloud.                                                             \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mTo cancel the simulation, use \u001b[32m'web.abort\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m or              \n",
       "\u001b[2;36m             \u001b[0m\u001b[32m'web.delete\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m or abort/delete the task in the web UI.      \n",
       "\u001b[2;36m             \u001b[0mTerminating the Python script will not stop the job running on the \n",
       "\u001b[2;36m             \u001b[0mcloud.                                                             \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e78561878c3948b3b0f8b63aaaf0fe9a",
       "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\">11:32:54 CET </span>status = preprocess                                                \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m11:32:54 CET\u001b[0m\u001b[2;36m \u001b[0mstatus = preprocess                                                \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\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">11:33:39 CET </span>starting up solver                                                 \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m11:33:39 CET\u001b[0m\u001b[2;36m \u001b[0mstarting up solver                                                 \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>running solver                                                     \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mrunning solver                                                     \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\">11:45:11 CET </span>status = postprocess                                               \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m11:45:11 CET\u001b[0m\u001b[2;36m \u001b[0mstatus = postprocess                                               \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5f39425e0e8b485b8f85f5b1722240c2",
       "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\">11:47:27 CET </span>status = success                                                   \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m11:47:27 CET\u001b[0m\u001b[2;36m \u001b[0mstatus = success                                                   \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": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "93a79db59bb14678a42bbfe88f83df9c",
       "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\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">11:47:37 CET </span>loading simulation from charge_junction.hdf5                       \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m11:47:37 CET\u001b[0m\u001b[2;36m \u001b[0mloading simulation from charge_junction.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\">11:47:38 CET </span><span style=\"color: #800000; text-decoration-color: #800000\">WARNING: Warning messages were found in the solver log. For more   </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #800000; text-decoration-color: #800000\">information, check </span><span style=\"color: #008000; text-decoration-color: #008000\">'SimulationData.log'</span><span style=\"color: #800000; text-decoration-color: #800000\"> or use                     </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'web.download_log(task_id)'</span><span style=\"color: #800000; text-decoration-color: #800000\">.                                       </span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m11:47:38 CET\u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING: Warning messages were found in the solver log. For more   \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[31minformation, check \u001b[0m\u001b[32m'SimulationData.log'\u001b[0m\u001b[31m or use                     \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[32m'web.download_log\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m\u001b[31m.                                       \u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#%matplotlib inline\n",
    "from tidy3d import web\n",
    "charge_data=web.run(charge_sim, task_name=\"charge_junction\", path=\"charge_junction.hdf5\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43917eb8-5125-45c8-bff0-a7d81c29ad51",
   "metadata": {},
   "source": [
    "### Post-process Charge simmulation\n",
    "Let's begin by visualizing the potential and electron fields at three different biases to make sure the solution looks reasonable. As it can be seen in the figure, as the (reverse) bias increases, the depletion region widens, as it is expected. The electric potential field also adapts to the applied biases and the amount of charge present in the waveguide area, which is also to be expected. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "722ee3fb-e9c2-47a5-bc41-7207f74bfcdd",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/momchil/miniconda3/envs/flex/lib/python3.12/site-packages/xarray/core/computation.py:818: RuntimeWarning: divide by zero encountered in log10\n",
      "  result_data = func(*input_data)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "voltages = charge_data[charge_3D_mnt.name].holes.values.voltage.data\n",
    "\n",
    "fig, ax = plt.subplots(2, 3)\n",
    "for n, index in enumerate([0,2,5]):\n",
    "    # let's read voltage first\n",
    "    charge_data[voltage_monitor_z0.name].potential.sel(voltage=voltages[index]).plot(ax=ax[0][n], grid=False)\n",
    "\n",
    "    # now let's plot some electrons\n",
    "    np.log10(charge_data[charge_3D_mnt.name].electrons.sel(z=0, voltage=voltages[index])).plot(ax=ax[1][n], grid=False)\n",
    "    ax[1][n].set_title(\"Bias {0:0.2f}V\".format(voltages[index]))\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ccde3ef-9fe6-4d05-9645-3dea63ac83fd",
   "metadata": {},
   "source": [
    "## 2. Experimental Data Validation\n",
    "\n",
    "In the second section of this example, we will validate the simulation numerical results with the experimental results from [1].\n",
    "\n",
    "### Small Signal Capacitance\n",
    "\n",
    "The [td.HeatChargeSimulationData](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.HeatChargeSimulationData.html) contains the extracted [td.SteadyCapacitanceData](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.SteadyCapacitanceData.html) specified by the monitor. The small signal capacitance is computed both with electrons and holes, and is averaged. As seen below, there is reasonable agreement between experimental data and simulation data (within the convergence tolerance)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "375f138a-5ee9-46e1-ae5b-f3f68eb8f02b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# capacitance from monitor - waveguide area\n",
    "CV_baehrjones = [\n",
    "    [-0.4, -0.25, 0, 0.25, 0.5, 0.75, 1, 1.5, 2, 3, 4],\n",
    "    [0.261, 0.248, 0.223, 0.208, 0.198, 0.190, 0.184, 0.175, 0.168, 0.157, 0.150]\n",
    "]\n",
    "\n",
    "mnt_v = np.array(charge_data[capacitance_global_mnt.name].electron_capacitance.coords[\"v\"].data)\n",
    "mnt_ce = np.array(charge_data[capacitance_global_mnt.name].electron_capacitance.data)\n",
    "mnt_ch = np.array(charge_data[capacitance_global_mnt.name].hole_capacitance.data)\n",
    "\n",
    "plt.plot(mnt_v, -0.5*(mnt_ce+mnt_ch) * 10 , 'k.-', label=\"Simulation Data\")\n",
    "plt.plot(CV_baehrjones[0], np.array(CV_baehrjones[1])*10, 'r-', label=\"Experimental Data\")\n",
    "\n",
    "plt.xlabel(\"Reverse bias (V)\")\n",
    "plt.ylabel(\"pF/cm\")\n",
    "plt.legend()\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76607757-8dc0-4d64-abad-aa06dda5b62b",
   "metadata": {},
   "source": [
    "### Charge-to-Optical Coupling\n",
    "\n",
    "In this section, we will explore how we can couple the Charge simulation data into an optical FDTD simulation. This can be implemented by defining a medium perturbation. This means that optical medium properties such as the refractive index change e.g. $\\Delta n \\propto \\varepsilon(N_e, N_h)$ and optical loss $\\alpha \\propto \\sigma(N_e, N_h)$ are proportional to changes to the permittivity $\\varepsilon$ and conductivity $\\sigma$ by the free electrons $N_e$ and free holes $N_h$ density.\n",
    "\n",
    "We will use empiric relationships presented in [M. Nedeljkovic, R. Soref and G. Z. Mashanovich, \"Free-Carrier Electrorefraction and Electroabsorption Modulation Predictions for Silicon Over the 1–14-  μm  Infrared Wavelength Range,\" IEEE Photonics Journal, vol. 3, no. 6, pp. 1171-1180, Dec. 2011](https://doi.org/10.1109/JPHOT.2011.2171930), that state that changes in $n$ and $k$ of Si can be described by formulas\n",
    "$$ - \\Delta n = \\frac{dn}{dN_e}(\\lambda) (\\Delta N_e)^{a(\\lambda)} + \\frac{dn}{dN_h}(\\lambda) (\\Delta N_h)^{\\beta(\\lambda)}$$\n",
    "$$ \\Delta \\left( \\frac{4 \\pi k}{\\lambda} \\right) = \\frac{dk}{dN_e}(\\lambda) (\\Delta N_e)^{\\gamma(\\lambda)} + \\frac{dk}{dN_h}(\\lambda) (\\Delta N_h)^{\\delta(\\lambda)}$$\n",
    "where $\\Delta N_e$ and $\\Delta N_h$ are electron and hole densities."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09934bae-8f75-4c7c-ac41-b8a4e4e0d465",
   "metadata": {},
   "source": [
    "We start by defining the optical frequency range of interest:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "35a0f839-6e34-416f-9121-5c29e05a0e4c",
   "metadata": {},
   "outputs": [],
   "source": [
    "wvl_um = 1.55\n",
    "freq0 = td.C_0 / wvl_um\n",
    "\n",
    "fwidth = freq0 / 5\n",
    "freqs = np.linspace(freq0 - fwidth / 10, freq0 + fwidth / 10, 201)\n",
    "wvls = td.C_0 / freqs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de201d9e-db1d-4e42-93bc-bceca3c4c266",
   "metadata": {},
   "source": [
    "We extract the real ($n$) and imaginary ($k$) parts of the refractive index from our silicon optical medium from our material library."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "41e4df07-3b99-4784-a5e5-e5b2f8bb9de2",
   "metadata": {},
   "outputs": [],
   "source": [
    "si = td.material_library['cSi']['Palik_Lossless']\n",
    "n_si, k_si = si.nk_model(frequency=td.C_0 / wvl_um)\n",
    "si_non_perturb = td.Medium.from_nk(n=n_si, k=k_si, freq=freq0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "098bb7a4-3f38-465b-a8b0-9b951c13853f",
   "metadata": {},
   "source": [
    "The charge parameters have the following values for wavelength of 1.55 $\\mu$m:\n",
    "\n",
    "| $\\lambda$ | $\\frac{dn}{dN_e}$ | $a$ | $\\frac{dn}{dN_h}$ | $\\beta$ | $\\frac{dk}{dN_e}$ | $\\gamma$ | $\\frac{dk}{dN_h}$ | $\\delta$ |\n",
    "| --------- | ----------------- | ------- | ------------------ | ------- | ----------------- | -------- | ----------------- | -------- |\n",
    "| $1.55$ | $5.40 \\times 10^{-22}$ | $1.011$ | $1.53 \\times 10^{-18}$ | $0.838$ | $8.88 \\times 10^{-21}$ | $1.167$ | $5.84 \\times 10^{-20}$ | $1.109$ | "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "2a32e171-be5f-4f2a-a705-7b54275d518a",
   "metadata": {},
   "outputs": [],
   "source": [
    "ne_coeff = -5.4e-22 * 1.1\n",
    "ne_pow = 1.011\n",
    "\n",
    "nh_coeff = -1.53e-18 * 1.1\n",
    "nh_pow = 0.838\n",
    "\n",
    "k_factor = wvl_um * 1e-4 / 4 / np.pi  # factor for conversion from absorption coefficient into k\n",
    "\n",
    "ke_coeff = k_factor * 8.88e-21 * 1.1\n",
    "ke_pow = 1.167\n",
    "\n",
    "kh_coeff = k_factor * 5.84e-20 * 1.1\n",
    "kh_pow = 1.109"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6518ca5-35b5-40f8-89e1-fe1c05c104bf",
   "metadata": {},
   "source": [
    "Given the nonlinear character of these dependencies we will incorporate them as sampled function on a rectangular grid formed by electron and hole density values. Specifically, we will sample the given $n$ and $k$ dependencies in the electron and hole density ranges up to $10^{20}$ 1/cm$^3$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "9cec2fc9-f7ae-4b2d-83db-b4d7deabaf32",
   "metadata": {},
   "outputs": [],
   "source": [
    "Ne_range = np.concatenate(([0], np.logspace(-6, 20, num=200)))\n",
    "Nh_range = np.concatenate(([0], np.logspace(-6, 20, num=200)))\n",
    "\n",
    "Ne_mesh, Nh_mesh = np.meshgrid(Ne_range, Nh_range, indexing='ij')\n",
    "\n",
    "dn_mesh = ne_coeff * Ne_mesh ** ne_pow + nh_coeff * Nh_mesh ** nh_pow\n",
    "dk_mesh = ke_coeff * Ne_mesh ** ke_pow + kh_coeff * Nh_mesh ** kh_pow"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bde528d4-6b9f-4039-b975-6a55f63fbcc9",
   "metadata": {},
   "source": [
    "### Perturbation Mediums Specifications\n",
    "\n",
    "We need to create an optical simulation. This means we need to slightly modify our existing `Scene` used in our `HeatChargeSimulation`. In order to do this, we will define the charge to optical coupling through a `PerturbationMedium`. This medium will enable us to input the numerical results from our `HeatChargeSimulationData` and change the optical medium properties."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2a003e3-c37e-4124-99fe-70c8345205c8",
   "metadata": {},
   "source": [
    "Now we convert sampled values of $n$ and $k$ into permittivity $\\varepsilon$ and conductivity $\\sigma$ values, and assemble a non-dispersive medium with perturbations (`PerturbationMedium`). Note that since the optical simulations don't yet support `MultiPhysicsMedium`, we need to change the scene medium."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "2a543e0b-a60a-416d-9ab9-e0345fe9164e",
   "metadata": {},
   "outputs": [],
   "source": [
    "dn_data = td.ChargeDataArray(dn_mesh, coords=dict(n=Ne_range, p=Nh_range))\n",
    "dk_data = td.ChargeDataArray(dk_mesh, coords=dict(n=Ne_range, p=Nh_range))\n",
    "\n",
    "n_si_charge = td.CustomChargePerturbation(perturbation_values=dn_data)\n",
    "k_si_charge = td.CustomChargePerturbation(perturbation_values=dk_data)\n",
    "\n",
    "n_si_perturbation = td.ParameterPerturbation(\n",
    "    charge=n_si_charge,\n",
    ")\n",
    "\n",
    "k_si_perturbation = td.ParameterPerturbation(\n",
    "    charge=k_si_charge,\n",
    ")\n",
    "\n",
    "si_perturb = td.PerturbationMedium.from_unperturbed(\n",
    "    medium=si_non_perturb,\n",
    "    perturbation_spec=td.IndexPerturbation(\n",
    "        delta_n=n_si_perturbation,\n",
    "        delta_k=k_si_perturbation,\n",
    "        freq=freq0,\n",
    "    )\n",
    ")\n",
    "\n",
    "new_structs = []\n",
    "for struct in charge_sim.structures:\n",
    "    # NOTE: applying perturbation material to Si\n",
    "    if struct.medium.name == Si_2D_doping.name:\n",
    "        new_structs.append(struct.updated_copy(medium=si_perturb))\n",
    "\n",
    "scene = td.Scene(\n",
    "    medium=SiO2.optical, # currently td.Simulation cannot accpet a MultiphysicsMedium\n",
    "    structures=new_structs,\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5ecfb28-6b62-4253-bec4-097a18270d64",
   "metadata": {},
   "source": [
    "### Creating an Optical Mode Simulation\n",
    "\n",
    "Let's create an optical mode simulation with our updated scene."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "8f96b875-e311-4d9b-b5af-6a5fd6301c28",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1500x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "span = 2*wvl_um\n",
    "\n",
    "port_center = (0,h_core,-span/2)\n",
    "port_size = (5, 5, 0)\n",
    "\n",
    "buffer = 1*wvl_um\n",
    "sim_size = (13+buffer, 10+buffer, span)\n",
    "\n",
    "bc_spec = td.BoundarySpec(\n",
    "    x = td.Boundary.pml(num_layers=20),\n",
    "    y = td.Boundary.pml(num_layers=30),\n",
    "    z = td.Boundary.periodic(),\n",
    ")\n",
    "\n",
    "sim = td.Simulation(\n",
    "    center=(0,0,0),\n",
    "    size=sim_size,\n",
    "    medium=scene.medium,\n",
    "    structures=scene.structures,\n",
    "    run_time=6e-12,\n",
    "    boundary_spec=bc_spec,\n",
    "    grid_spec=td.GridSpec.auto(min_steps_per_wvl=60, wavelength=wvl_um),\n",
    ")\n",
    "\n",
    "_, ax = plt.subplots(1, 2, figsize=(15, 7))\n",
    "sim.plot(y=sim.center[1], ax=ax[0])\n",
    "sim.plot(z=sim.center[2], ax=ax[1])\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b1d03e3-4bb3-4540-b61b-ac6eac1eda5b",
   "metadata": {},
   "source": [
    "### Coupling our `SteadyFreeCarrierData` into our `PerturbationMedium`: \n",
    "\n",
    "We already have configured our reference optical mode simulation with the default free carrier distribution. However, our [td.HeatChargeSimulationData](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.HeatChargeSimulationData.html) with from our [td.SteadyFreeCarrierData](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.SteadyFreeCarrierData.html) can be used to *perturb* our silicon optical medium. Let's see how we can do this below.\n",
    "\n",
    "We can create a list of perturbed optical simulation by using the function `perturbed_mediums_copy()`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "b8ed2bec-9e68-4fae-aad5-1a79d4607b46",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def apply_charge(charge_data):\n",
    "    perturbed_sims = []\n",
    "    for n, v in enumerate(charge_data[charge_monitor_z0_big.name].electrons.values.voltage.data):\n",
    "        e_data = charge_data[charge_monitor_z0_big.name].electrons.sel(voltage=v)\n",
    "        h_data = charge_data[charge_monitor_z0_big.name].holes.sel(voltage=v)\n",
    "        perturbed_sims.append(\n",
    "            sim.perturbed_mediums_copy(\n",
    "                electron_density=e_data, \n",
    "                hole_density=h_data,\n",
    "            )\n",
    "        )\n",
    "    return perturbed_sims\n",
    "\n",
    "perturbed_sims = apply_charge(charge_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e5dc29f-6560-414d-ba06-1974a73b5d37",
   "metadata": {},
   "source": [
    "Let's check what the difference in permittivity looks like at different applied voltages:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "6a0ae7fa-5cbb-491c-98f9-00c28ba7bfd5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_, ax = plt.subplots(1, 2, figsize=(15, 5))\n",
    "\n",
    "sampling_region = td.Box(center=(0, h_core/2, 0), size=(6, 5, 1))\n",
    "eps_undoped = sim.epsilon(box=sampling_region).sel(z=0, method=\"nearest\")\n",
    "\n",
    "for ax_ind, ind in enumerate([0, len(voltages)-1]):\n",
    "    eps_doped = perturbed_sims[ind].epsilon(box=sampling_region).sel(z=0, method=\"nearest\")\n",
    "    eps_doped = eps_doped.interp(x=eps_undoped.x, y=eps_undoped.y)\n",
    "    eps_diff = np.abs(np.real(eps_doped - eps_undoped))\n",
    "    eps_diff.plot(x=\"x\", ax=ax[ax_ind])\n",
    "    \n",
    "    ax[ax_ind].set_aspect(\"equal\")\n",
    "    ax[ax_ind].set_title(f\"Bias: {voltages[ind]:1.1f} V\")\n",
    "    ax[ax_ind].set_xlabel(\"x (um)\")\n",
    "    ax[ax_ind].set_ylabel(\"y (um)\")\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63c52225-f21e-4107-9193-62d2d89e3357",
   "metadata": {},
   "source": [
    "### Running Waveguide Mode Simulations\n",
    "\n",
    "Instead of running a full FDTD simulation, we will compute the modes of the waveguide. \n",
    "This is a smaller computation that will also highlight the influence of free carriers fields over the refraction coefficient. \n",
    "\n",
    "We will compute the waveguide modes on a plane centered around the core section of the device:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "61d912e6-dcd9-4f29-afae-676141e13c07",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tidy3d.plugins.mode import ModeSolver"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "8b6d918d-013c-46b1-bf41-ea0aff9d7681",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mode_plane = td.Box(size=port_size)\n",
    "\n",
    "# visualize\n",
    "ax = sim.plot(z = 0)\n",
    "mode_plane.plot(z=0, ax=ax, alpha=0.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "9fd71e4e-6f38-48f6-a06a-962c075d3adc",
   "metadata": {},
   "outputs": [],
   "source": [
    "mode_solvers = dict()\n",
    "i = 0\n",
    "for psim in perturbed_sims:\n",
    "    ms = ModeSolver(\n",
    "        simulation=psim,\n",
    "        plane=mode_plane,\n",
    "        freqs=np.linspace(freqs[0], freqs[-1], 11),\n",
    "        mode_spec=td.ModeSpec(num_modes=1, precision=\"double\"),\n",
    "    )\n",
    "    mode_solvers[i] = ms\n",
    "    i +=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "936261c8-e0db-4d22-b3ed-de97e2d987bf",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "20e9fbc276cd42eba221072fc5c89023",
       "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\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">11:57:01 CET </span>Started working on Batch containing <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">6</span> tasks.                       \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m11:57:01 CET\u001b[0m\u001b[2;36m \u001b[0mStarted working on Batch containing \u001b[1;36m6\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\">11:57:15 CET </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.123</span> for the whole batch.                \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m11:57:15 CET\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m0.123\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": "8d845859e78b42e1909b98a29e77c276",
       "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\">12:00:23 CET </span>Batch complete.                                                    \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m12:00:23 CET\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": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "81f51a272dc94d46b943e9923a6b1d7b",
       "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"
    }
   ],
   "source": [
    "# server mode computation\n",
    "ms_batch_data = web.Batch(simulations=mode_solvers, reduce_simulation=True).run()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f67fe20-51ae-4c39-bed0-b589f38f47b3",
   "metadata": {},
   "source": [
    "### Relative Phase Change $\\Delta \\phi$\n",
    "\n",
    "Based on the computed effective refractive index $n_{eff}$, we can now determine both phase shift $\\Delta \\phi$ and $\\alpha$ optical loss per cm as a function of the applied bias voltage $V_n$ using the following equations from [1]:\n",
    "\n",
    "\n",
    "$$\n",
    "\\Delta n_{\\text{eff}} = \\text{Re}(n_{\\text{eff}}(V_n) - n_{\\text{eff}}({V_0}))\n",
    "$$\n",
    "\n",
    "$$\n",
    "\\Delta \\phi = \\frac{2\\pi \\Delta n_{\\text{eff}}}{\\lambda_0}\n",
    "$$\n",
    "\n",
    "$$\n",
    "\\alpha_{\\text{dB/cm}} = 10 \\cdot 4 \\pi \\cdot \\text{Im}(n_{\\text{eff}}) / \\lambda_0 \\cdot 10^4 \\cdot \\log_{10}(e)\n",
    "$$\n",
    "\n",
    "We compare this to simulation data from another popular simulation software."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "e478d33e-f2cc-4684-b8f8-13da24c5a591",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n_eff_freq0 = [md.n_complex.sel(f=freq0, mode_index=0).values for md in ms_batch_data.values()]\n",
    "\n",
    "ind_V0 = 1\n",
    "delta_neff = np.real(n_eff_freq0 - n_eff_freq0[ind_V0])\n",
    "rel_phase_change = 2*np.pi*delta_neff/wvl_um * 1e4\n",
    "alpha_dB_cm = 10*4*np.pi*np.imag(n_eff_freq0)/wvl_um*1e4*np.log10(np.exp(1))\n",
    "\n",
    "# other results\n",
    "v_other = [-0.5, 0, 0.4, 0.7, 1.5, 2.4, 3.8]\n",
    "pc_other=[-0.91, 0, 0.6, 1,   1.8, 2.7, 3.6]\n",
    "loss_other = [4.9, 4.55, 4.34, 4.15, 3.77, 3.41, 2.95]\n",
    "\n",
    "_, ax = plt.subplots(1, 2, figsize=(15, 4))\n",
    "ax[0].plot(voltages, rel_phase_change, 'k.-', label=\"Simulation Data\")\n",
    "ax[0].plot(v_other, pc_other, 'r-', label=\"Other Software\")\n",
    "ax[0].set_xlabel(\"Reverse bias (V)\")\n",
    "ax[0].set_ylabel(\"Relative phase (rad/cm)\")\n",
    "ax[0].grid()\n",
    "ax[0].legend()\n",
    "\n",
    "ax[1].plot(voltages, alpha_dB_cm, 'k.-', label=\"Simulation Data\")\n",
    "ax[1].plot(v_other, loss_other, 'r-', label=\"Other Software\")\n",
    "ax[1].set_xlabel(\"Reverse bias (V)\")\n",
    "ax[1].set_ylabel(\"Loss (dB/cm)\")\n",
    "ax[1].grid()\n",
    "ax[1].legend()\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea47a4cd-5a3c-4d20-919f-61ea4c19e0cc",
   "metadata": {},
   "source": [
    "As it can be seen, we'd need to apply a bias of ~3V in order to get a phase shift of $\\pi$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0487c2ac-c5b3-4207-b653-c74dd04ddab8",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "description": "This notebook demonstrates how to use Tidy3D's charge solver to simulate a silicon electro-optic phase modulator.",
  "feature_image": ".img/charge_modulator.png",
  "kernelspec": {
   "display_name": "flex",
   "language": "python",
   "name": "python3"
  },
  "keywords": "electro-optic, modulator, charge solver, pn-junction, Tidy3D, FDTD",
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.7"
  },
  "title": "Charge Solver: a silicon electro-optic phase modulator | Flexcompute"
 },
 "nbformat": 4,
 "nbformat_minor": 5
}