{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Small-signal AC analysis of a silicon PIN photodiode\n",
        "\n",
        "## Introduction to Small-signal Analysis\n",
        "\n",
        "Small-Signal AC (SSAC) analysis is a technique used to characterize the\n",
        "frequency-dependent behavior of semiconductor devices under small\n",
        "perturbations around a DC operating point. This method is essential for\n",
        "understanding the high-frequency performance limitations of devices such\n",
        "as photodiodes, modulators, and transistors. We first establish a DC\n",
        "operating point (bias condition) for the device. Then, we apply a small\n",
        "AC voltage perturbation and analyze how the device responds at different\n",
        "frequencies. The analysis assumes that the perturbation is small enough\n",
        "that the device’s response remains linear, allowing us to extract\n",
        "frequency-dependent parameters.\n",
        "\n",
        "The primary output of SSAC analysis is the **small-signal admittance**\n",
        "\\$ Y() \\$, which relates the small-signal current \\$ I() \\$ to the\n",
        "small-signal voltage \\$ V() \\$:\n",
        "\n",
        "$$Y(\\omega) = \\frac{I(\\omega)}{V(\\omega)} = G(\\omega) + j\\omega C(\\omega)$$\n",
        "\n",
        "where: - \\$ G() \\$ is the **conductance** (real part of admittance),\n",
        "representing resistive losses - \\$ C() \\$ is the **capacitance**\n",
        "(imaginary part divided by $\\omega$), representing charge storage - \\$ =\n",
        "2f \\$ is the angular frequency\n",
        "\n",
        "### Goal of this notebook\n",
        "\n",
        "In this notebook, we will:\n",
        "\n",
        "1.  Set up a reverse-biased silicon PIN photodiode structure\n",
        "2.  Perform SSAC analysis over a range of frequencies\n",
        "3.  Extract and plot the frequency-dependent conductance \\$ G(f) \\$ and\n",
        "    capacitance \\$ C(f) \\$\n",
        "\n",
        "<img src=\"img/vertical_pin.png\" width=\"500\" alt=\"Schematic\">\n",
        "\n",
        "## Setup and imports"
      ],
      "id": "46e33819-553d-44a7-8e1d-d06fe0ccdbf2"
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {},
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import tidy3d as td\n",
        "import tidy3d.web as web"
      ],
      "id": "aa8932dc"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Device and simulation setup\n",
        "\n",
        "### PIN photodiode structure\n",
        "\n",
        "We will design a vertical PIN photodiode with the following structure: -\n",
        "**P+ region**: Heavily doped p-type silicon (top contact) - **Intrinsic\n",
        "region**: Lightly doped silicon (absorption region) - **N+ region**:\n",
        "Heavily doped n-type silicon (bottom contact) - **Metal contacts**:\n",
        "Aluminum electrodes for electrical connection\n",
        "\n",
        "The device will be operated under reverse bias (negative voltage on\n",
        "p-side), which depletes the intrinsic region and creates a strong\n",
        "electric field for efficient photocarrier collection.\n",
        "\n",
        "<img src=\"img/vertical_pin.png\" width=\"500\" alt=\"Vertical pin schematic\">\n",
        "\n",
        "### Geometry definition\n",
        "\n",
        "First, we define the geometric parameters of our PIN structure. All\n",
        "dimensions are in micrometers (µm)."
      ],
      "id": "2cafa527-c280-4c76-b6c2-b93b810fc2f6"
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Device dimensions (µm)\n",
        "device_width = 2.0  # Lateral dimension\n",
        "z_size = 2.0  # Extrusion dimension (for 2D simulation)\n",
        "\n",
        "# Layer thicknesses\n",
        "p_thickness = 0.3  # P+ contact layer\n",
        "i_thickness = 1.0  # Intrinsic absorption region\n",
        "n_thickness = 0.3  # N+ contact layer\n",
        "contact_thickness = 0.2  # Metal contact thickness\n",
        "\n",
        "# Layer centers (stacked vertically along y-axis)\n",
        "total_height = p_thickness + i_thickness + n_thickness\n",
        "y_bottom = -total_height / 2\n",
        "\n",
        "# N+ layer (bottom)\n",
        "n_center_y = y_bottom + n_thickness / 2\n",
        "n_center = (0, n_center_y, 0)\n",
        "n_size = (device_width, n_thickness, z_size)\n",
        "\n",
        "# Intrinsic layer (middle)\n",
        "i_center_y = n_center_y + n_thickness / 2 + i_thickness / 2\n",
        "i_center = (0, i_center_y, 0)\n",
        "i_size = (device_width, i_thickness, z_size)\n",
        "\n",
        "# P+ layer (top)\n",
        "p_center_y = i_center_y + i_thickness / 2 + p_thickness / 2\n",
        "p_center = (0, p_center_y, 0)\n",
        "p_size = (device_width, p_thickness, z_size)\n",
        "\n",
        "# Contact layers\n",
        "contact_n_y = n_center_y - n_thickness / 2 - contact_thickness / 2\n",
        "contact_p_y = p_center_y + p_thickness / 2 + contact_thickness / 2\n",
        "\n",
        "contact_n_center = (0, contact_n_y, 0)\n",
        "contact_p_center = (0, contact_p_y, 0)\n",
        "contact_size = (device_width, contact_thickness, z_size)"
      ],
      "id": "50d68272"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Doping profile\n",
        "\n",
        "Next, we define the doping profiles for the PIN structure. We use: -\n",
        "**Constant doping** for the lightly doped intrinsic region (background\n",
        "doping) - **Gaussian doping** for the heavily doped P+ and N+ regions\n",
        "with smooth transitions\n",
        "\n",
        "The doping concentrations are based on typical values for silicon\n",
        "photodiodes."
      ],
      "id": "7aaca8ac-9b33-4e6d-b485-0db30426a11a"
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Doping concentrations (cm^-3)\n",
        "intrinsic_doping = 1e14  # Background doping in intrinsic region\n",
        "p_plus_doping = 1e19  # P+ contact doping\n",
        "n_plus_doping = 1e19  # N+ contact doping\n",
        "\n",
        "# Junction width for Gaussian doping profiles\n",
        "junction_width = 0.01  # Controls sharpness of doping transition\n",
        "\n",
        "# Intrinsic region: constant low doping\n",
        "acceptor_intrinsic = td.ConstantDoping(center=i_center, size=i_size, concentration=intrinsic_doping)\n",
        "\n",
        "# P+ region: Gaussian acceptor doping\n",
        "acceptor_p_plus = td.GaussianDoping(\n",
        "    center=p_center,\n",
        "    size=p_size,\n",
        "    concentration=p_plus_doping,\n",
        "    ref_con=intrinsic_doping,\n",
        "    width=junction_width,\n",
        "    source=\"ymax\",  # Doping is constant along ymax plane\n",
        ")\n",
        "\n",
        "# N+ region: Gaussian donor doping\n",
        "donor_n_plus = td.GaussianDoping(\n",
        "    center=n_center,\n",
        "    size=n_size,\n",
        "    concentration=n_plus_doping,\n",
        "    ref_con=intrinsic_doping,\n",
        "    width=junction_width,\n",
        "    source=\"ymin\",  # Doping is constant along ymin plane\n",
        ")"
      ],
      "id": "460d8b0d"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Material models\n",
        "\n",
        "We now define the physical models for silicon. The parameters are based\n",
        "on well-established models from semiconductor device physics literature."
      ],
      "id": "48625339-7e86-4684-ba31-01abc2fb0f75"
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Electron mobility model (Caughey-Thomas)\n",
        "mobility_n = td.CaugheyThomasMobility(\n",
        "    mu=1471.0,  # Maximum mobility (cm²/V·s)\n",
        "    mu_min=52.2,  # Minimum mobility (cm²/V·s)\n",
        "    ref_N=9.68e16,  # Reference doping (cm⁻³)\n",
        "    exp_N=0.68,  # Doping exponent\n",
        "    exp_1=-0.57,  # Temperature exponent 1\n",
        "    exp_2=-2.33,  # Temperature exponent 2\n",
        "    exp_3=2.4,  # Temperature exponent 3\n",
        "    exp_4=-0.146,  # Temperature exponent 4\n",
        ")\n",
        "\n",
        "# Hole mobility model (Caughey-Thomas)\n",
        "mobility_p = td.CaugheyThomasMobility(\n",
        "    mu=470.5,  # Maximum mobility (cm²/V·s)\n",
        "    mu_min=44.9,  # Minimum mobility (cm²/V·s)\n",
        "    ref_N=2.23e17,  # Reference doping (cm⁻³)\n",
        "    exp_N=0.719,  # Doping exponent\n",
        "    exp_1=-0.57,  # Temperature exponent 1\n",
        "    exp_2=-2.23,  # Temperature exponent 2\n",
        "    exp_3=2.4,  # Temperature exponent 3\n",
        "    exp_4=-0.146,  # Temperature exponent 4\n",
        ")\n",
        "\n",
        "# Effective density of states (constant for isothermal simulation)\n",
        "# Values at T=300K from Sze & Ng\n",
        "N_c = td.ConstantEffectiveDOS(N=2.86e19)  # Conduction band (cm⁻³)\n",
        "N_v = td.ConstantEffectiveDOS(N=3.1e19)  # Valence band (cm⁻³)\n",
        "\n",
        "# Energy bandgap at 300K\n",
        "E_g = td.ConstantEnergyBandGap(eg=1.12)  # Silicon bandgap (eV)"
      ],
      "id": "06bdb9b7"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Recombination models\n",
        "\n",
        "We include the major recombination mechanisms in silicon: -\n",
        "**Shockley-Read-Hall (SRH)**: Recombination through trap states. -\n",
        "**Radiative**: Band-to-band recombination. - **Auger**: Three-particle\n",
        "recombination."
      ],
      "id": "fd2fe1fd-1a19-4b4c-b357-6b878cf0f3f2"
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Shockley-Read-Hall recombination\n",
        "# Carrier lifetimes for moderately pure silicon\n",
        "srh_recombination = td.ShockleyReedHallRecombination(\n",
        "    tau_n=1e-6,  # Electron lifetime (s)\n",
        "    tau_p=1e-6,  # Hole lifetime (s)\n",
        ")\n",
        "\n",
        "# Radiative recombination\n",
        "radiative_recombination = td.RadiativeRecombination(\n",
        "    r_const=1.6e-14  # Radiative recombination coefficient (cm³/s)\n",
        ")\n",
        "\n",
        "# Auger recombination\n",
        "auger_recombination = td.AugerRecombination(\n",
        "    c_n=2.8e-31,  # Electron Auger coefficient (cm⁶/s)\n",
        "    c_p=9.9e-32,  # Hole Auger coefficient (cm⁶/s)\n",
        ")"
      ],
      "id": "36569154"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Bandgap Narrowing\n",
        "\n",
        "Since we have high doping levels in the contact regions, bandgap\n",
        "narrowing is expected to impact the device characteristics. We take this\n",
        "effect into consideration by applying the **Slotboom model**."
      ],
      "id": "1ad43fc2-4133-41e8-84e5-453c49cf23ce"
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Bandgap narrowing model (Slotboom)\n",
        "bandgap_narrowing = td.SlotboomBandGapNarrowing(\n",
        "    v1=6.92e-3,  # Narrowing coefficient (eV)\n",
        "    n2=1.3e17,  # Reference concentration (cm⁻³)\n",
        "    c2=0.5,  # Exponent\n",
        "    min_N=1e15,  # Minimum doping for narrowing (cm⁻³)\n",
        ")"
      ],
      "id": "0267846e"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Create MultiPhysics Media\n",
        "\n",
        "We now assemble all the material properties into `MultiPhysicsMedium`\n",
        "objects for use in the simulation."
      ],
      "id": "4fefd4f3-6f48-4f9f-82d0-d45a77f854f3"
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Silicon semiconductor medium with all doping and physical models\n",
        "si_charge = td.SemiconductorMedium(\n",
        "    permittivity=11.7,\n",
        "    N_c=N_c,\n",
        "    N_v=N_v,\n",
        "    E_g=E_g,\n",
        "    N_d=[donor_n_plus],  # List of donor doping profiles\n",
        "    N_a=[acceptor_intrinsic, acceptor_p_plus],  # List of acceptor doping profiles\n",
        "    mobility_n=mobility_n,\n",
        "    mobility_p=mobility_p,\n",
        "    R=[srh_recombination, radiative_recombination, auger_recombination],\n",
        "    delta_E_g=bandgap_narrowing,\n",
        ")\n",
        "\n",
        "# Silicon as a MultiPhysicsMedium\n",
        "Si_medium = td.MultiPhysicsMedium(charge=si_charge, name=\"Si_doped\")\n",
        "\n",
        "# Aluminum contacts (high conductivity metal)\n",
        "Al_medium = td.MultiPhysicsMedium(\n",
        "    charge=td.ChargeConductorMedium(conductivity=3.5e7 * 1e-6),  # Convert to µm units\n",
        "    name=\"Al\",\n",
        ")\n",
        "\n",
        "# Air as background medium\n",
        "air = td.MultiPhysicsMedium(name=\"air\")"
      ],
      "id": "821e7ec4"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Create Geometry Structures"
      ],
      "id": "b298b610-32ab-4703-ac25-2dfe49c071a5"
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Silicon structures\n",
        "si_p_structure = td.Structure(\n",
        "    geometry=td.Box(center=p_center, size=p_size), medium=Si_medium, name=\"si_p_region\"\n",
        ")\n",
        "\n",
        "si_i_structure = td.Structure(\n",
        "    geometry=td.Box(center=i_center, size=i_size), medium=Si_medium, name=\"si_i_region\"\n",
        ")\n",
        "\n",
        "si_n_structure = td.Structure(\n",
        "    geometry=td.Box(center=n_center, size=n_size), medium=Si_medium, name=\"si_n_region\"\n",
        ")\n",
        "\n",
        "# Metal contact structures\n",
        "contact_p_structure = td.Structure(\n",
        "    geometry=td.Box(center=contact_p_center, size=contact_size), medium=Al_medium, name=\"contact_p\"\n",
        ")\n",
        "\n",
        "contact_n_structure = td.Structure(\n",
        "    geometry=td.Box(center=contact_n_center, size=contact_size), medium=Al_medium, name=\"contact_n\"\n",
        ")\n",
        "\n",
        "# Combine all structures\n",
        "structures = [\n",
        "    si_n_structure,\n",
        "    si_i_structure,\n",
        "    si_p_structure,\n",
        "    contact_n_structure,\n",
        "    contact_p_structure,\n",
        "]"
      ],
      "id": "4b007085"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Visualize the Device Structure\n",
        "\n",
        "Before running the simulation, let’s visualize the device geometry and\n",
        "doping profile."
      ],
      "id": "475adaa7-5478-4611-8803-61487ec9a7c1"
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {},
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "image/png": 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PO3WfwKwXecuXL+9wqU1brrfVslatWmX1dY0aNay+drSM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\nLYMJAG5u77//vg4fPqzU1FQtXLhQDz74oLdLumH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jZw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\nW0mSJEmSJEmSJEkK2P8B+gI85mHQR/YAAAAASUVORK5CYII=\n"
          }
        }
      ],
      "source": [
        "# Create scene for visualization\n",
        "scene = td.Scene(structures=structures, medium=air)\n",
        "\n",
        "# Plot geometry and doping\n",
        "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 5))\n",
        "\n",
        "# Plot structure geometry\n",
        "scene.plot(z=0, ax=ax1)\n",
        "ax1.set_title(\"PIN Photodiode Cross-Section\")\n",
        "ax1.set_xlabel(\"x (µm)\")\n",
        "ax1.set_ylabel(\"y (µm)\")\n",
        "\n",
        "# Plot doping profile\n",
        "scene.plot_structures_property(z=0, property=\"doping\", ax=ax2, scale=\"symlog\")\n",
        "ax2.set_title(\"Net Doping Concentration (cm⁻³)\")\n",
        "ax2.set_xlabel(\"x (µm)\")\n",
        "ax2.set_ylabel(\"y (µm)\")\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "id": "6f9db5ac"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Boundary conditions and SSAC analysis setup\n",
        "\n",
        "Now we define the electrical boundary conditions for the SSAC analysis:\n",
        "\n",
        "1.  **Cathode (N-contact)**: Grounded at 0V.\n",
        "2.  **Anode (P-contact)**: SSAC analysis voltage source with DC bias and\n",
        "    small-signal perturbation.\n",
        "\n",
        "We apply a **reverse bias** to the photodiode to: - Deplete the\n",
        "intrinsic region. - Create a strong electric field for fast carrier\n",
        "collection. - Minimize junction capacitance."
      ],
      "id": "4ea55459-a13d-4a42-8878-90b7e6d798c5"
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {},
      "outputs": [],
      "source": [
        "# DC bias voltages for reverse bias operation\n",
        "reverse_bias_voltages = [-2]  # -2V reverse bias\n",
        "\n",
        "# Small-signal amplitude (must be small for linear approximation)\n",
        "ac_amplitude = 1e-3  # 1 mV perturbation\n",
        "\n",
        "# Cathode: ground reference (N-contact)\n",
        "bc_cathode = td.HeatChargeBoundarySpec(\n",
        "    condition=td.VoltageBC(source=td.GroundVoltage()),\n",
        "    placement=td.StructureStructureInterface(structures=[\"contact_n\", \"si_n_region\"]),\n",
        ")\n",
        "\n",
        "# Anode: SSAC voltage source (P-contact)\n",
        "bc_anode = td.HeatChargeBoundarySpec(\n",
        "    condition=td.VoltageBC(\n",
        "        source=td.SSACVoltageSource(voltage=reverse_bias_voltages, amplitude=ac_amplitude)\n",
        "    ),\n",
        "    placement=td.StructureStructureInterface(structures=[\"contact_p\", \"si_p_region\"]),\n",
        ")\n",
        "\n",
        "boundary_specs = [bc_cathode, bc_anode]"
      ],
      "id": "f4f22825"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Frequency range for SSAC analysis\n",
        "\n",
        "We define a logarithmic frequency sweep from 100 Mhz to 100 GHz to\n",
        "capture the frequency-dependent behavior."
      ],
      "id": "3f456d9d-f336-4d93-a330-ee14ee0d3c89"
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Frequency range for SSAC analysis (Hz)\n",
        "freq_min = 1e8  # 100 Mhz\n",
        "freq_max = 1e11  # 100 GHz\n",
        "n_freqs_per_decade = 5  # Number of frequency points per decade\n",
        "\n",
        "freq_range = td.FreqRange.from_freq_interval(freq_min, freq_max)\n",
        "freqs = freq_range.sweep_decade(n_freqs_per_decade)"
      ],
      "id": "205b6ac8"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Define Monitors\n",
        "\n",
        "We set up monitors to record various quantities during the simulation."
      ],
      "id": "c3b66386-03f5-4cf8-b133-a8673f9dc51b"
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Monitor for free carrier concentration\n",
        "carrier_monitor = td.SteadyFreeCarrierMonitor(\n",
        "    center=(0, 0, 0), size=(td.inf, td.inf, td.inf), name=\"carriers\", unstructured=True\n",
        ")\n",
        "\n",
        "# Monitor for electric potential\n",
        "potential_monitor = td.SteadyPotentialMonitor(\n",
        "    center=(0, 0, 0), size=(td.inf, td.inf, td.inf), name=\"potential\", unstructured=True\n",
        ")\n",
        "\n",
        "monitors = [carrier_monitor, potential_monitor]"
      ],
      "id": "7fbf7401"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Analysis and Solver Settings\n",
        "\n",
        "We configure the SSAC analysis parameters and solver tolerance settings."
      ],
      "id": "61071543-a03e-491d-99a0-23d9603bb536"
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Solver tolerance settings (defaults)\n",
        "charge_tolerance = td.ChargeToleranceSpec()\n",
        "\n",
        "# SSAC analysis specification\n",
        "analysis_spec = td.IsothermalSSACAnalysis(\n",
        "    tolerance_settings=charge_tolerance,\n",
        "    convergence_dv=0.1,  # Voltage convergence criterion (V)\n",
        "    fermi_dirac=False,  # Use Maxwell-Boltzmann statistics (faster, adequate for moderate doping)\n",
        "    temperature=300,  # Ambient temperature (K)\n",
        "    freqs=freqs.tolist(),  # Frequency list for AC analysis\n",
        ")"
      ],
      "id": "0a3bbb65"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Grid specification\n",
        "\n",
        "We define the mesh for the simulation. A finer mesh near the junctions\n",
        "ensures accurate resolution of the space-charge regions where the\n",
        "electric field varies rapidly."
      ],
      "id": "41cfa1bf-6463-4e1e-8d67-7a490cab7434"
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Base mesh resolution\n",
        "dl_bulk = 0.04  # Bulk mesh size (µm)\n",
        "dl_interface = 0.015  # Interface mesh size (µm)\n",
        "\n",
        "# Refined regions near P-I and I-N junctions\n",
        "refinement_pi = td.GridRefinementRegion(\n",
        "    center=(0, p_center_y - p_thickness / 2, 0),\n",
        "    size=(device_width, 0.2, 0),\n",
        "    dl_internal=dl_interface / 2,\n",
        "    transition_thickness=dl_bulk * 10,\n",
        ")\n",
        "\n",
        "refinement_in = td.GridRefinementRegion(\n",
        "    center=(0, n_center_y + n_thickness / 2, 0),\n",
        "    size=(device_width, 0.2, 0),\n",
        "    dl_internal=dl_interface / 2,\n",
        "    transition_thickness=dl_bulk * 10,\n",
        ")\n",
        "\n",
        "# Grid specification\n",
        "grid_spec = td.DistanceUnstructuredGrid(\n",
        "    relative_min_dl=0,\n",
        "    dl_interface=dl_interface,\n",
        "    dl_bulk=dl_bulk * 20,\n",
        "    distance_interface=2 * dl_interface,\n",
        "    distance_bulk=6 * dl_interface,\n",
        "    sampling=1000,\n",
        "    uniform_grid_mediums=[\"Si_doped\"],\n",
        "    mesh_refinements=[refinement_pi, refinement_in],\n",
        ")"
      ],
      "id": "7ea30504"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Create the Simulation\n",
        "\n",
        "Finally, we assemble all components into a `HeatChargeSimulation`\n",
        "object."
      ],
      "id": "713a815d-27cb-4605-a727-036e76932c07"
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Simulation domain\n",
        "sim_center = (0, 0, 0)\n",
        "sim_size = (device_width * 1.2, total_height + 2 * contact_thickness + 0.2, 0)\n",
        "\n",
        "# Create simulation\n",
        "sim = td.HeatChargeSimulation(\n",
        "    medium=air,\n",
        "    structures=structures,\n",
        "    size=sim_size,\n",
        "    center=sim_center,\n",
        "    boundary_spec=boundary_specs,\n",
        "    analysis_spec=analysis_spec,\n",
        "    monitors=monitors,\n",
        "    grid_spec=grid_spec,\n",
        ")"
      ],
      "id": "bd90c1bd"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Run the Simulation\n",
        "\n",
        "Now we run the SSAC simulation using the Tidy3D backend solver. The\n",
        "simulation will first solve the DC problem at the specified bias\n",
        "voltage. Then, it will perform small-signal analysis at each frequency\n",
        "and extract AC current response."
      ],
      "id": "fdd52f3e-eb9a-40c8-82a6-e524963b558b"
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {},
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "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\">11:25:55 UTC </span>Created task <span style=\"color: #008000; text-decoration-color: #008000\">'heat_charge_2026-07-21_11-25-55_mesh'</span> with           \n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>resource_id <span style=\"color: #008000; text-decoration-color: #008000\">'vom-6a224fa5-3846-46b0-a7f3-09516b4ead2a'</span> and         \n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>task_type <span style=\"color: #008000; text-decoration-color: #008000\">'VOLUME_MESH'</span>.                                           \n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "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 VolumeMesher solver is currently in the beta stage. Cost  \n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>of VolumeMesher simulations is subject to change in the future.    \n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<script type=\"application/vnd.jupyter.widget-view+json\">\n",
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              "</script>"
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        {
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          "metadata": {},
          "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>"
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        },
        {
          "output_type": "display_data",
          "metadata": {},
          "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\">11:25:57 UTC </span>Estimated FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.025</span>. Use <span style=\"color: #008000; text-decoration-color: #008000\">'web.real_cost(task_id)'</span> to  \n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>get the billed FlexCredit cost after a simulation run.             \n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "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\">11:25:58 UTC </span>status = queued                                                    \n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "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>"
            ]
          }
        },
        {
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          "metadata": {},
          "data": {
            "text/html": [
              "<script type=\"application/vnd.jupyter.widget-view+json\">\n",
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              "</script>"
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        {
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          "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>"
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        },
        {
          "output_type": "display_data",
          "metadata": {},
          "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\">11:27:50 UTC </span>starting up solver                                                 \n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "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\">11:27:51 UTC </span>running solver                                                     \n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "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\">11:27:54 UTC </span>status = success                                                   \n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "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>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "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\">11:27:55 UTC </span>Created task <span style=\"color: #008000; text-decoration-color: #008000\">'heat_charge_2026-07-21_11-25-55_solve'</span> with          \n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>resource_id <span style=\"color: #008000; text-decoration-color: #008000\">'hec-d00062fa-5f92-4f1a-af37-6e8786167c3d'</span> and         \n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>task_type <span style=\"color: #008000; text-decoration-color: #008000\">'HEAT_CHARGE'</span>.                                           \n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "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>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<script type=\"application/vnd.jupyter.widget-view+json\">\n",
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              "</script>"
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          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "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>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "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\">11:27:56 UTC </span>Estimated typical FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.086</span>. For charge simulations,  \n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>the billed cost depends on the number of solver iterations required\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>for convergence.                                                   \n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "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>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.401</span>. This assumes the charge solver     \n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>reaches its configured iteration limits for all applied biases. Use\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'web.real_cost(task_id)'</span> to get the billed FlexCredit cost after a \n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>simulation run.                                                    \n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "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\">11:27:59 UTC </span>status = queued                                                    \n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "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>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<script type=\"application/vnd.jupyter.widget-view+json\">\n",
              "{\"model_id\":\"a0a2d018d6744e998c4ca937985a5ec5\",\"version_major\":2,\"version_minor\":0,\"quarto_mimetype\":\"application/vnd.jupyter.widget-view+json\"}\n",
              "</script>"
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        {
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          "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\">11:28:13 UTC </span>status = preprocess                                                \n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "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>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "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\">11:28:27 UTC </span>starting up solver                                                 \n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "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>"
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          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "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\">11:28:35 UTC </span>status = success                                                   \n",
              "</pre>"
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          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "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>"
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          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<script type=\"application/vnd.jupyter.widget-view+json\">\n",
              "{\"model_id\":\"5eb2fb0803854de5a4a03f5138344fb6\",\"version_major\":2,\"version_minor\":0,\"quarto_mimetype\":\"application/vnd.jupyter.widget-view+json\"}\n",
              "</script>"
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        },
        {
          "output_type": "display_data",
          "metadata": {},
          "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>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "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\">11:28:37 UTC </span>Loading results from simulation_data.hdf5                          \n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "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><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>"
            ]
          }
        }
      ],
      "source": [
        "results = web.run(sim)"
      ],
      "id": "abb05ea0"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Post-processing and results analysis\n",
        "\n",
        "### Extract AC current data\n",
        "\n",
        "The SSAC simulation provides complex-valued AC current $I(v, f)$ as a\n",
        "function of DC bias voltage $v$ and frequency $f$ . This current\n",
        "represents the device’s small-signal response to the AC voltage\n",
        "perturbation. Note that since Charge follows the passive sign convention\n",
        "we need to multiply by -1 for the present setup."
      ],
      "id": "6fff24d0-fc66-43da-8252-11b70814f4fd"
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {},
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
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          }
        }
      ],
      "source": [
        "# Extract AC current-voltage data\n",
        "ac_current = -results.device_characteristics.ac_current_voltage\n",
        "\n",
        "# Get the bias voltage and frequency arrays\n",
        "bias_voltage = reverse_bias_voltages[0]\n",
        "\n",
        "frequencies = ac_current.coords[\"f\"].values  # Frequency array\n",
        "\n",
        "# Select current at the specified bias voltage\n",
        "# Note: For 2D simulations, multiply by device depth to get total current\n",
        "device_depth = 50  # µm (assuming device extends 50 µm in z-direction)\n",
        "I_ac = -ac_current.sel(v=bias_voltage, method=\"nearest\").values * device_depth\n",
        "\n",
        "fig, ax = plt.subplots(1, 1, figsize=(12, 5))\n",
        "# Plot conductance\n",
        "ax.loglog(\n",
        "    frequencies / 1e6,\n",
        "    I_ac.real,\n",
        "    \"o-\",\n",
        "    linewidth=2,\n",
        "    markersize=6,\n",
        "    label=f\"Real(I) {bias_voltage} Volt\",\n",
        ")\n",
        "ax.loglog(\n",
        "    frequencies / 1e6,\n",
        "    I_ac.imag,\n",
        "    \"*-\",\n",
        "    linewidth=2,\n",
        "    markersize=6,\n",
        "    label=f\"Imag(I) {bias_voltage} Volt\",\n",
        ")\n",
        "ax.set_xlabel(\"Frequency (MHz)\", fontsize=12)\n",
        "ax.set_ylabel(\"Current (A)\", fontsize=12)\n",
        "ax.set_title(\"Current plot\", fontsize=13, fontweight=\"bold\")\n",
        "ax.grid(True, which=\"both\", alpha=0.3, linestyle=\"--\")\n",
        "ax.legend(fontsize=11)\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "id": "dd66ba11"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Calculate admittance, conductance, and capacitance\n",
        "\n",
        "From the AC current $I(\\omega)$ and the applied AC voltage amplitude\n",
        "$V_0$ , we calculate:\n",
        "\n",
        "1.  **Admittance**: $Y(\\omega)$ = \\$ \\$\n",
        "\n",
        "2.  **Conductance**: $G(\\omega)$ = $\\text{Re}[Y(\\omega)]$ — represents\n",
        "    resistive losses\n",
        "\n",
        "3.  **Capacitance**: $C(\\omega)$ = $\\frac{\\text{Im}[Y(\\omega)]}{\\omega}$\n",
        "    — represents charge storage\n",
        "\n",
        "where $\\omega = 2\\pi f$ is the angular frequency.\n",
        "\n",
        "### Plot frequency-dependent conductance\n",
        "\n",
        "Let’s create a combined plot showing both conductance $G(f)$ and\n",
        "capacitance $C(f)$ to visualize the complete admittance behavior."
      ],
      "id": "1eb21928-69d4-4735-950e-0f7496b33ca0"
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Calculate admittance Y = I / V\n",
        "Y = I_ac / ac_amplitude  # Admittance (S)\n",
        "\n",
        "# Extract real and imaginary parts\n",
        "G = np.real(Y)  # Conductance (S)\n",
        "B = np.imag(Y)  # Susceptance (S)\n",
        "\n",
        "# Calculate capacitance from susceptance: C = B / ω = B / (2πf)\n",
        "omega = 2 * np.pi * frequencies  # Angular frequency (rad/s)\n",
        "C = B / omega  # Capacitance (F)\n",
        "\n",
        "# Convert to more convenient units\n",
        "C_pF = C * 1e12  # Capacitance in pF\n",
        "G_uS = G * 1e6  # Conductance in µS"
      ],
      "id": "50d976a8"
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {},
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "image/png": 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blfTeV3P00UfD4XAoLml066234u9//3vWbQSDwZSBFEcffTTsdnvGMayursbS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\n5b7vfve7GftPRERERERERERERFTJ3u3uxxb3UFleu9pmxV7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8EsPDw1i+fDlmz56NI444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o4OKU8DgQCCwaBqWT255HA4UurQ7XZrvge6urqkv4eGhhAKhVTLFpJLWnWo1E8Jh8MpOSLPbz25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\nuSWWkV7qVqco+cOu4yctdQqr4yc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IB6O/s9pLav5wWl4yooPFvMT6WHxWHfP2ktLfo2M+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\nD+LgwYMQBAF33nknnnvuObjdbtx5550499xzTXu/YrGI5eVlAEChUEA0GjX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dAgDwzW9+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Zgvhw8fln/u9Xryz9lsFt1ud1N7nueH9rAul8tot9uqx4/FYvJKS81mE5VKRbVtIBBAKBTC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\nLY2fNuuQdLpcLs36wAnjp1GUXpLa9no9VX+wPH5S62tZG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HgwYNTBLDTz/9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\nPhhCoVAIqzmZu3+bm8kn7AwZMgRDhgzR+Nm8efOEbbKGDBmCefPmmTpbo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          }
        }
      ],
      "source": [
        "fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(12, 6))\n",
        "\n",
        "# Plot conductance\n",
        "ax1.loglog(\n",
        "    frequencies / 1e6, G_uS, \"o-\", linewidth=2, markersize=6, color=\"#2E86AB\", label=\"Conductance G\"\n",
        ")\n",
        "ax1.set_xlabel(\"Frequency (MHz)\", fontsize=12)\n",
        "ax1.set_ylabel(\"Conductance (µS)\", fontsize=12)\n",
        "ax1.set_title(\"Real Part of Admittance: Conductance\", fontsize=13, fontweight=\"bold\")\n",
        "ax1.grid(True, which=\"both\", alpha=0.3, linestyle=\"--\")\n",
        "ax1.legend(fontsize=11)\n",
        "\n",
        "# Theoretical capacitance calculation\n",
        "eps0 = 8.854e-18  # F/µm\n",
        "eps_si = 11.7\n",
        "area = (device_width + 2 * junction_width) * device_depth  # µm^2\n",
        "width = i_thickness  # µm\n",
        "C_theoretical = (td.EPSILON_0 * Si_medium.charge.permittivity * area) / width  # in Farads\n",
        "C_theoretical_pF = C_theoretical * 1e12  # in pF\n",
        "\n",
        "# Plot capacitance\n",
        "ax2.loglog(frequencies / 1e6, C_pF, \"o-\", linewidth=2, markersize=6, label=\"Capacitance C\")\n",
        "ax2.axhline(\n",
        "    y=C_theoretical_pF,\n",
        "    color=\"k\",\n",
        "    linestyle=\"--\",\n",
        "    label=f\"Theoretical C ({C_theoretical_pF:.4f} pF)\",\n",
        ")\n",
        "ax2.set_xlabel(\"Frequency (MHz)\", fontsize=12)\n",
        "ax2.set_ylabel(\"Capacitance (pF)\", fontsize=12)\n",
        "ax2.set_title(\"Imaginary Part of Admittance: Capacitance\", fontsize=13, fontweight=\"bold\")\n",
        "ax2.grid(True, which=\"both\", alpha=0.3, linestyle=\"--\")\n",
        "ax2.legend(fontsize=11)\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "id": "3d336a38"
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {},
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Relative difference capacitance: 0.63%"
          ]
        }
      ],
      "source": [
        "print(\n",
        "    f\"Relative difference capacitance: {100 * np.abs((C_theoretical_pF - C_pF[0]) / C_theoretical_pF):.2f}%\"\n",
        ")"
      ],
      "id": "4131f796"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Interpretation of results\n",
        "\n",
        "The capacitance $C(f)$ we observe is primarily the **junction\n",
        "capacitance** of the reverse-biased PIN diode. The SSAC analysis\n",
        "confirms that the device behaves as a geometric capacitor in this\n",
        "frequency regime. By extracting the imaginary part of the admittance\n",
        "($C \\approx \\text{Im}(Y) / \\omega$), we observe a frequency-independent\n",
        "capacitance. This indicates that the intrinsic region is fully depleted\n",
        "and acts effectively as a dielectric spacer between the doped contacts.\n",
        "The simulated value aligns with the analytical parallel-plate\n",
        "approximation $C \\approx \\epsilon_0 \\epsilon_r A / W$, with a relative\n",
        "difference of less than 1%.\n",
        "\n",
        "## Conclusion\n",
        "\n",
        "In this notebook, we have showcased the Tidy3d capabilities through the\n",
        "following tasks:\n",
        "\n",
        "1.  **Defined the geometry of a silicon PIN photodiode** with realistic\n",
        "    doping profiles and material parameters.\n",
        "2.  **Set up and performed Small-Signal AC (SSAC) analysis** using\n",
        "    small-signal analysis framework.\n",
        "3.  **Extracted frequency-dependent device parameters** including\n",
        "    conductance $G(f)$ and capacitance $C(f)$."
      ],
      "id": "e12ff869-bfd2-4dc8-a0cc-6a18d5bd8fe7"
    }
  ],
  "nbformat": 4,
  "nbformat_minor": 5,
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    }
  }
}