CoupledLineBandpassFilter.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "726c359f-f07d-4997-884a-7bb6f0f49884",
   "metadata": {},
   "source": [
    "# Designing and optimizing a coupled line bandpass filter\n",
    "Microwave filters serve as essential components in microwave and radio frequency communication systems. These critical building blocks help us exert control over the frequencies that are transmitted through a system. Implementing microwave filters can be achieved through various methods, for example, utilizing lumped elements, sections of transmission lines, or a combination of both.\n",
    "\n",
    "In this notebook, we will guide you through the process of designing and validating a bandpass filter using multiple sections of coupled microstrip lines. This practical application example is based on design specifications from the study [Ragani Taoufik, N. Amar Touhami, and M. Agoutane, \"Designing a Microstrip coupled line bandpass filter\", International Journal of Engineering and Technology 2(4), 266-269 (2013)](https://doi.org/10.14419/ijet.v2i4.1173). The specifications outlined in the study are aimed at creating a bandpass filter centered at 6 GHz with a bandwidth of 2 GHz.\n",
    "\n",
    "<img src=\"img/coupled_line_bandpass_filter.png\" width=\"400\" alt=\"Schematic of a coupled line bandpass filter\">\n",
    "\n",
    "\n",
    "To take it a step further, we will also demonstrate how to design an improved coupled line filter that is more closely centered at 6 GHz with an improved return loss. This will be achieved by leveraging the powerful `optimize` module from the widely-used `SciPy` package. By the end of this notebook, you will have a deeper understanding of how to use `Tidy3D` and the microwave plugin to design, simulate, and validate microwave networks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c39755d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Tidy3d imports\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.ticker as ticker\n",
    "\n",
    "# External modules needed for this notebook\n",
    "import numpy as np\n",
    "\n",
    "# uncomment the following line to install scikit-rf if it's not installed in your environment already\n",
    "# pip install scikit-rf\n",
    "import skrf as rf  # For validation using circuit models of the filter\n",
    "import tidy3d as td\n",
    "import tidy3d.plugins.microwave as mw\n",
    "\n",
    "# Tidy3d plugin imports\n",
    "import tidy3d.plugins.smatrix as smatrix\n",
    "from scipy import (\n",
    "    optimize as opt,\n",
    ")\n",
    "\n",
    "# We set the logging level to \"ERROR\". Otherwise there are numerous warnings due to the proximity of the structure to PML boundaries.\n",
    "td.config.logging_level = \"ERROR\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54d17567",
   "metadata": {},
   "source": [
    "## Filter Design and Microstrip Parameters\n",
    "\n",
    "We start from the coupled line filter proposed by Taoufik *et al*. [1]. In [1], the filter is designed using the insertion loss method, which is described in [2]. Although a full explanation of the insertion loss method is out of the scope of this notebook, we summarize the main four steps below:\n",
    "\n",
    "1. Filter specifications: The initial step in designing a filter using the insertion loss method involves defining the desired filter response and the type of filter to be used. This includes outlining the passband and stopband frequencies, the maximum allowable insertion loss in the passband, the minimum required attenuation in the stopband, and the specific filter type (such as Butterworth, Chebyshev, Elliptic, etc.) best suited for the application.\n",
    "2. Low-pass prototype design: Next, the order of the filter, which is the number of reactive elements, is determined based on the specified requirements. The order of the filter is typically chosen to achieve the desired attenuation in the stopband.\n",
    "3. Computation of prototype element values: In this step, the values for the prototype elements of the filter are computed based on the filter type and order. These calculations are done using mathematical formulas specific to the filter type. Alternatively, precomputed tables for standard filter types can be used to look up the prototype element values.\n",
    "4. Implementation of elements: Finally, the computed prototype element values are implemented as physical elements in the filter. Depending on the design and frequency requirements, these elements can be realized as lumped elements (inductors and capacitors) or as sections of transmission lines. In the case of a coupled lined filter, the lumped element values are transformed into a list of coupled line sections with desired even and odd characteristic line impedances, see [2].\n",
    "\n",
    "First, we will simulate the filter as designed and implemented in [1]. Later, we will optimize the filter implementation by finding better values for the widths, lengths, and gaps of each segment. The designed filter in [1] is made up of four segments with the parameters given below:\n",
    "\n",
    "| Segment  | Width (mm) |  Length (mm) |  Gap (mm) |\n",
    "| -------- | ---------- | ------------ | --------- |\n",
    "| 1 | 1.4 | 6 | 0.2 |\n",
    "| 2 | 2.4 | 6 | 0.2 |\n",
    "| 3 | 2.4 | 6 | 0.2 |\n",
    "| 4 | 1.4 | 6 | 0.2 |\n",
    "\n",
    "\n",
    "Using the tabulated parameters from [1], a `Tidy3D` simulation is set up that uses the [TerminalComponentModeler](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.plugins.smatrix.TerminalComponentModeler.html) to extract scattering parameters from this 2-port device."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0d22504b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Frequency range of interest is 2-10 GHz\n",
    "freq0 = 6e9\n",
    "# A sample frequency that is within the stopband of the filter\n",
    "freq_stopband = 3e9\n",
    "freqs = np.linspace(2, 10, 101) * 1e9\n",
    "fwidth = 0.5 * (np.max(freqs) - np.min(freqs))\n",
    "lda0 = td.C_0 / freq0  # Central wavelength\n",
    "\n",
    "# Materials used in this simulation\n",
    "PEC = td.PECMedium()\n",
    "eps_sub = 4.3  # Substrate permittivity\n",
    "sub_medium = td.Medium(permittivity=eps_sub)  # define substrate medium\n",
    "\n",
    "mm = 1e3  # Scaling used for millimeters\n",
    "# Large value to ensure structures extend outside simulation domain\n",
    "inf_eff = 1000 * mm\n",
    "\n",
    "# Geometry parameters common to all designs\n",
    "h_sub = 1.56 * mm  # Substrate thickness\n",
    "h_trace = 0.035 * mm  # Thickness of metal layers\n",
    "\n",
    "# Microstrip parameters taken from [1]\n",
    "strips_W = [1.4 * mm, 2.4 * mm, 2.4 * mm, 1.4 * mm]\n",
    "strips_L = [6 * mm, 6 * mm, 6 * mm, 6 * mm]\n",
    "strips_G = [0.2 * mm, 0.2 * mm, 0.2 * mm, 0.2 * mm]\n",
    "\n",
    "# Numpy arrays of the microstrip dimensions\n",
    "lengths = np.array(strips_L)\n",
    "widths = np.array(strips_W)\n",
    "gaps = np.array(strips_G)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3ce27ba",
   "metadata": {},
   "source": [
    "To facilitate the creation of the coupled line filter geometry, we define two helper functions below that generate coupled microstrips given lists of their lengths, widths, and gaps."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9801758a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# This helper function generates the starting positions of the coupled microstrip line segments\n",
    "def get_coupled_line_geometry(lengths, widths, gaps):\n",
    "    # Coupled lines are generated from upper left to lower right centered at the origin\n",
    "    total_length = np.sum(lengths)\n",
    "    # Compute the total width\n",
    "    total_width = 0\n",
    "    prev_width = 0\n",
    "    # Coupled line segments are aligned differently depending on their relative widths\n",
    "    for width, gap in zip(widths, gaps):\n",
    "        total_width += width + gap\n",
    "        if width > prev_width:\n",
    "            total_width += width - prev_width\n",
    "        prev_width = width\n",
    "\n",
    "    # Compute the starting positions of each segment the left bound and the top bound\n",
    "    xstarts = []\n",
    "    ystarts = []\n",
    "\n",
    "    prev_xstart = -total_length / 2\n",
    "    prev_ystart = total_width / 2\n",
    "    prev_length = 0\n",
    "    prev_width = 0\n",
    "    prev_gap = 0\n",
    "    for length, width, gap in zip(lengths, widths, gaps):\n",
    "        # Compute x position of the segment (left bound)\n",
    "        xstarts.append(prev_xstart + prev_length)\n",
    "\n",
    "        # Compute y position of the segment (top bound)\n",
    "        ystart = prev_ystart - prev_width - prev_gap\n",
    "        if width < prev_width:\n",
    "            ystart += width - prev_width\n",
    "        ystarts.append(ystart)\n",
    "\n",
    "        prev_xstart = xstarts[-1]\n",
    "        prev_ystart = ystarts[-1]\n",
    "        prev_length = length\n",
    "        prev_width = width\n",
    "        prev_gap = gap\n",
    "\n",
    "    return total_length, total_width, xstarts, ystarts\n",
    "\n",
    "\n",
    "# Generate the tidy3d structures representing the coupled line segments\n",
    "# using the previously computed positions\n",
    "def generate_coupled_lines(lengths, widths, gaps, xstarts, ystarts):\n",
    "    coupled_lines = []\n",
    "    # Each segment is composed of two microstrips\n",
    "    for length, width, gap, xstart, ystart in zip(lengths, widths, gaps, xstarts, ystarts):\n",
    "        coupled_lines.append(\n",
    "            td.Structure(\n",
    "                geometry=td.Box.from_bounds(\n",
    "                    rmin=(xstart, ystart - width, 0),\n",
    "                    rmax=(xstart + length, ystart, h_trace),\n",
    "                ),\n",
    "                medium=PEC,\n",
    "            )\n",
    "        )\n",
    "        coupled_lines.append(\n",
    "            td.Structure(\n",
    "                geometry=td.Box.from_bounds(\n",
    "                    rmin=(xstart, ystart - 2 * width - gap, 0),\n",
    "                    rmax=(xstart + length, ystart - width - gap, h_trace),\n",
    "                ),\n",
    "                medium=PEC,\n",
    "            )\n",
    "        )\n",
    "    return coupled_lines"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0afe1325",
   "metadata": {},
   "source": [
    "Now we may easily create and view the structure."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "882e2718",
   "metadata": {},
   "outputs": [
    {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create the structures and geometry of the coupled line bandpass filter\n",
    "(total_L, total_W, xstarts, ystarts) = get_coupled_line_geometry(lengths, widths, gaps)\n",
    "coupled_lines = generate_coupled_lines(lengths, widths, gaps, xstarts, ystarts)\n",
    "# Create the substrate block\n",
    "substrate = td.Structure(\n",
    "    geometry=td.Box.from_bounds(rmin=(-inf_eff, -inf_eff, -inf_eff), rmax=(inf_eff, inf_eff, 0)),\n",
    "    medium=sub_medium,\n",
    ")\n",
    "# Define the simulation domain size with some extra padding based on central wavelength\n",
    "Lx = total_L + lda0 / 5\n",
    "Ly = total_W + lda0 / 5\n",
    "Lz = h_sub + lda0 / 8\n",
    "\n",
    "# View the created line coupler\n",
    "fig, ax = plt.subplots()\n",
    "for geo in coupled_lines:\n",
    "    geo.plot(z=0, ax=ax)\n",
    "# Formatter to help plotting in units of millimeters\n",
    "formatter = ticker.FuncFormatter(lambda y, _: f\"{(1e-3) * y:g}\")\n",
    "xlbl = \"x (mm)\"\n",
    "ylbl = \"y (mm)\"\n",
    "# Update plot labels\n",
    "ax.xaxis.set_major_formatter(formatter)\n",
    "ax.yaxis.set_major_formatter(formatter)\n",
    "ax.set_xlabel(xlbl)\n",
    "ax.set_ylabel(ylbl)\n",
    "ax.set_xlim(-Lx / 2, Lx / 2)\n",
    "ax.set_ylim(-Ly / 2, Ly / 2)\n",
    "ax.set_title(\"Top view of coupler from [1]\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10621860",
   "metadata": {},
   "source": [
    "With the structure created, we now set up the `Tidy3D` Simulation that will be used by the [TerminalComponentModeler](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.plugins.smatrix.TerminalComponentModeler.html).\n",
    "\n",
    "First, we define mesh override structures so that the small height of the microstrips is captured accurately. In addition, we ensure that there are plenty of cells along the width and length of the structure where the fields will be strongly confined."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a69d7761",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Definition of mesh overrides\n",
    "mesh_overrides = [\n",
    "    # The first mesh override ensures the small height of the strips is accurately modeled.\n",
    "    td.MeshOverrideStructure(\n",
    "        geometry=td.Box(\n",
    "            center=[0, 0, h_trace / 2],\n",
    "            size=[1.1 * total_L, 1.1 * total_W, h_trace],\n",
    "        ),\n",
    "        dl=[total_L / 200, np.min(widths) / 20, h_trace],\n",
    "    ),\n",
    "    # The second mesh override refined the grid within the substrate.\n",
    "    td.MeshOverrideStructure(\n",
    "        geometry=td.Box(\n",
    "            center=[0, 0, -h_sub / 2],\n",
    "            size=[1.1 * total_L, 1.1 * total_W, h_sub],\n",
    "        ),\n",
    "        dl=[total_L / 200, np.min(widths) / 20, h_sub / 20],\n",
    "    ),\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d7e1131",
   "metadata": {},
   "source": [
    "Now, the rest of the components are set up for the `Tidy3D` simulation. Note the use of a PEC boundary condition for the minus *z* axis, which simplifies the modeling of the microstrips' ground plane."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d68b9243",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Field monitor to view the electromagnetic fields along the propagation direction.\n",
    "field_monitor = td.FieldMonitor(\n",
    "    center=(0, 0, -h_sub / 2),\n",
    "    size=(td.inf, td.inf, 0),\n",
    "    freqs=[freq_stopband, freq0],\n",
    "    name=\"field\",\n",
    ")\n",
    "# Boundary conditions are perfectly matched layers, except for the minus z boundary.\n",
    "boundary_spec = td.BoundarySpec(\n",
    "    x=td.Boundary.pml(),\n",
    "    y=td.Boundary.pml(),\n",
    "    z=td.Boundary(minus=td.PECBoundary(), plus=td.PML()),\n",
    ")\n",
    "# The base tidy3D Simulation ready to be used by the TerminalComponentModeler\n",
    "sim = td.Simulation(\n",
    "    center=(0, 0, Lz / 2 - h_sub),\n",
    "    size=(Lx, Ly, Lz),\n",
    "    grid_spec=td.GridSpec.auto(\n",
    "        min_steps_per_wvl=40.0,\n",
    "        wavelength=lda0,\n",
    "        dl_min=h_trace / 5,\n",
    "        override_structures=mesh_overrides,\n",
    "    ),\n",
    "    structures=[substrate] + coupled_lines,\n",
    "    sources=[],\n",
    "    monitors=[field_monitor],\n",
    "    run_time=10e-9,\n",
    "    boundary_spec=boundary_spec,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b1f685f",
   "metadata": {},
   "source": [
    "## Setting up the TerminalComponentModeler\n",
    "Lumped ports are now added at the left and right sides of the filter. The lumped ports are used in the [TerminalComponentModeler](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.plugins.smatrix.TerminalComponentModeler.html) to set up simulations for extracting the scattering parameters of the network. Each port represents an additional simulation that needs to be run in order to extract the full scattering matrix of the system."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f8ed594a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compute port xy locations\n",
    "port_left = (xstarts[0], ystarts[0] - widths[0] / 2)\n",
    "port_right = (xstarts[-1] + lengths[-1], ystarts[-1] - gaps[-1] - 1.5 * widths[-1])\n",
    "# Impedance at each port\n",
    "reference_impedance = 50\n",
    "port_1_td = smatrix.LumpedPort(\n",
    "    center=(port_left[0], port_left[1], -h_sub / 2),\n",
    "    size=(0, widths[0], h_sub),\n",
    "    voltage_axis=2,\n",
    "    name=\"lumped_port_1\",\n",
    "    impedance=reference_impedance,\n",
    ")\n",
    "\n",
    "port_2_td = smatrix.LumpedPort(\n",
    "    center=(port_right[0], port_right[1], -h_sub / 2),\n",
    "    size=(0, widths[-1], h_sub),\n",
    "    voltage_axis=2,\n",
    "    name=\"lumped_port_2\",\n",
    "    impedance=reference_impedance,\n",
    ")\n",
    "\n",
    "modeler = smatrix.TerminalComponentModeler(\n",
    "    simulation=sim,\n",
    "    ports=[port_1_td, port_2_td],\n",
    "    freqs=freqs,\n",
    "    verbose=True,\n",
    ")\n",
    "\n",
    "# Before running the simulations, we can view the mesh to get an idea of how the mesh overrides have performed.\n",
    "# The simulation objects created within the terminal component modeler will take port mesh refinement into account.\n",
    "sim_port1 = list(modeler.sim_dict.values())[0]\n",
    "\n",
    "f, (ax1, ax2) = plt.subplots(1, 2, tight_layout=True, figsize=(15, 5))\n",
    "hlim = [-Lx / 2, Lx / 2]\n",
    "vlim = [-Ly / 2, Ly / 2]\n",
    "ax = sim_port1.plot(z=h_trace / 2, ax=ax1)\n",
    "sim_port1.plot_grid(z=h_trace / 2, ax=ax1, hlim=hlim, vlim=vlim)\n",
    "# Update plot labels\n",
    "ax.xaxis.set_major_formatter(formatter)\n",
    "ax.yaxis.set_major_formatter(formatter)\n",
    "ax.set_xlabel(xlbl)\n",
    "ax.set_ylabel(ylbl)\n",
    "ax.set_title(\"Top view of coupler\")\n",
    "\n",
    "hlim = [port_2_td.bounds[0][0] - 1 * mm, port_2_td.bounds[1][0] + 1 * mm]\n",
    "vlim = [sim.bounds[0][2] - 100, h_trace + 500]\n",
    "ax = sim_port1.plot(y=port_2_td.center[1], ax=ax2)\n",
    "sim_port1.plot_grid(y=h_trace / 2, ax=ax, hlim=hlim, vlim=vlim)\n",
    "# Update plot labels\n",
    "ax.xaxis.set_major_formatter(formatter)\n",
    "ax.yaxis.set_major_formatter(formatter)\n",
    "ax.set_xlabel(xlbl)\n",
    "ax.set_ylabel(\"z (mm)\")\n",
    "ax.set_title(\"Side view of port 2\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d9476e25",
   "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\">14:10:12 EDT </span>Created task <span style=\"color: #008000; text-decoration-color: #008000\">'smatrix_lumped_port_1'</span> with task_id                  \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'fdve-35a1065f-81fb-48e4-8c78-3deb47a2e241'</span> and task_type <span style=\"color: #008000; text-decoration-color: #008000\">'FDTD'</span>.  \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:10:12 EDT\u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'smatrix_lumped_port_1'\u001b[0m with task_id                  \n",
       "\u001b[2;36m             \u001b[0m\u001b[32m'fdve-35a1065f-81fb-48e4-8c78-3deb47a2e241'\u001b[0m and task_type \u001b[32m'FDTD'\u001b[0m.  \n"
      ]
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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\">             </span>View task using web UI at                                          \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-35a1065f-81fb-48e4-8c78-3deb47a2e241\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'https://tidy3d.simulation.cloud/workbench?taskId=fdve-35a1065f-81f</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-35a1065f-81fb-48e4-8c78-3deb47a2e241\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">b-48e4-8c78-3deb47a2e241'</span></a>.                                         \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mView task using web UI at                                          \n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=867532;https://tidy3d.simulation.cloud/workbench?taskId=fdve-35a1065f-81fb-48e4-8c78-3deb47a2e241\u001b\\\u001b[32m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=114878;https://tidy3d.simulation.cloud/workbench?taskId=fdve-35a1065f-81fb-48e4-8c78-3deb47a2e241\u001b\\\u001b[32mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=867532;https://tidy3d.simulation.cloud/workbench?taskId=fdve-35a1065f-81fb-48e4-8c78-3deb47a2e241\u001b\\\u001b[32m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=163206;https://tidy3d.simulation.cloud/workbench?taskId=fdve-35a1065f-81fb-48e4-8c78-3deb47a2e241\u001b\\\u001b[32mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=867532;https://tidy3d.simulation.cloud/workbench?taskId=fdve-35a1065f-81fb-48e4-8c78-3deb47a2e241\u001b\\\u001b[32m-35a1065f-81f\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=867532;https://tidy3d.simulation.cloud/workbench?taskId=fdve-35a1065f-81fb-48e4-8c78-3deb47a2e241\u001b\\\u001b[32mb-48e4-8c78-3deb47a2e241'\u001b[0m\u001b]8;;\u001b\\.                                         \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">14:10:13 EDT </span>Created task <span style=\"color: #008000; text-decoration-color: #008000\">'smatrix_lumped_port_2'</span> with task_id                  \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'fdve-b58f8e87-5ab5-432d-a37f-4fe136ed6f80'</span> and task_type <span style=\"color: #008000; text-decoration-color: #008000\">'FDTD'</span>.  \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:10:13 EDT\u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'smatrix_lumped_port_2'\u001b[0m with task_id                  \n",
       "\u001b[2;36m             \u001b[0m\u001b[32m'fdve-b58f8e87-5ab5-432d-a37f-4fe136ed6f80'\u001b[0m and task_type \u001b[32m'FDTD'\u001b[0m.  \n"
      ]
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>View task using web UI at                                          \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-b58f8e87-5ab5-432d-a37f-4fe136ed6f80\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'https://tidy3d.simulation.cloud/workbench?taskId=fdve-b58f8e87-5ab</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-b58f8e87-5ab5-432d-a37f-4fe136ed6f80\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">5-432d-a37f-4fe136ed6f80'</span></a>.                                         \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mView task using web UI at                                          \n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=678450;https://tidy3d.simulation.cloud/workbench?taskId=fdve-b58f8e87-5ab5-432d-a37f-4fe136ed6f80\u001b\\\u001b[32m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=562417;https://tidy3d.simulation.cloud/workbench?taskId=fdve-b58f8e87-5ab5-432d-a37f-4fe136ed6f80\u001b\\\u001b[32mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=678450;https://tidy3d.simulation.cloud/workbench?taskId=fdve-b58f8e87-5ab5-432d-a37f-4fe136ed6f80\u001b\\\u001b[32m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=76995;https://tidy3d.simulation.cloud/workbench?taskId=fdve-b58f8e87-5ab5-432d-a37f-4fe136ed6f80\u001b\\\u001b[32mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=678450;https://tidy3d.simulation.cloud/workbench?taskId=fdve-b58f8e87-5ab5-432d-a37f-4fe136ed6f80\u001b\\\u001b[32m-b58f8e87-5ab\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=678450;https://tidy3d.simulation.cloud/workbench?taskId=fdve-b58f8e87-5ab5-432d-a37f-4fe136ed6f80\u001b\\\u001b[32m5-432d-a37f-4fe136ed6f80'\u001b[0m\u001b]8;;\u001b\\.                                         \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4d70633bc0b540b5af38377cc228f6ea",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">14:10:20 EDT </span>Started working on Batch.                                          \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:10:20 EDT\u001b[0m\u001b[2;36m \u001b[0mStarted working on 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\">14:10:22 EDT </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.207</span> for the whole batch.                \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:10:22 EDT\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m0.207\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": "a6c1f2d9c3574458aab20efea0317301",
       "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\">14:11:21 EDT </span>Batch complete.                                                    \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:11:21 EDT\u001b[0m\u001b[2;36m \u001b[0mBatch complete.                                                    \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "91a40b6b712f4986b83e7473e86a25bb",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">14:11:23 EDT </span>loading simulation from                                            \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>.<span style=\"color: #800080; text-decoration-color: #800080\">/</span><span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">fdve-35a1065f-81fb-48e4-8c78-3deb47a2e241.hdf5</span>                   \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:11:23 EDT\u001b[0m\u001b[2;36m \u001b[0mloading simulation from                                            \n",
       "\u001b[2;36m             \u001b[0m.\u001b[35m/\u001b[0m\u001b[95mfdve-35a1065f-81fb-48e4-8c78-3deb47a2e241.hdf5\u001b[0m                   \n"
      ]
     },
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       "Output()"
      ]
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     "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": []
     },
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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\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">14:11:24 EDT </span>loading simulation from                                            \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>.<span style=\"color: #800080; text-decoration-color: #800080\">/</span><span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">fdve-b58f8e87-5ab5-432d-a37f-4fe136ed6f80.hdf5</span>                   \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:11:24 EDT\u001b[0m\u001b[2;36m \u001b[0mloading simulation from                                            \n",
       "\u001b[2;36m             \u001b[0m.\u001b[35m/\u001b[0m\u001b[95mfdve-b58f8e87-5ab5-432d-a37f-4fe136ed6f80.hdf5\u001b[0m                   \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Run the jobs and save the scattering parameters\n",
    "s_matrix = modeler.run()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d46f596",
   "metadata": {},
   "source": [
    "## Plotting Fields\n",
    "\n",
    "The frequency-domain fields have been recorded over the extent of the coupled line filter between the ground plane and microstrips. Although not needed for extracting scattering parameters, the plots below demonstrate the effect of the filter on frequencies within and frequencies outside the passband. In these plots, the excited port is located in the upper left of the figure, and the transmission port is located at the bottom right."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3a41d1e7",
   "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\">14:11:25 EDT </span>loading simulation from                                            \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>.<span style=\"color: #800080; text-decoration-color: #800080\">/</span><span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">fdve-35a1065f-81fb-48e4-8c78-3deb47a2e241.hdf5</span>                   \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:11:25 EDT\u001b[0m\u001b[2;36m \u001b[0mloading simulation from                                            \n",
       "\u001b[2;36m             \u001b[0m.\u001b[35m/\u001b[0m\u001b[95mfdve-35a1065f-81fb-48e4-8c78-3deb47a2e241.hdf5\u001b[0m                   \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1100x300 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sim1 = modeler.batch.load()[\"smatrix_lumped_port_1\"]\n",
    "\n",
    "f, (ax1, ax2) = plt.subplots(1, 2, tight_layout=True, figsize=(11, 3))\n",
    "sim1.plot_field(field_monitor_name=\"field\", field_name=\"Ez\", val=\"abs\", f=freq_stopband, ax=ax1)\n",
    "# Update plot labels\n",
    "ax1.xaxis.set_major_formatter(formatter)\n",
    "ax1.yaxis.set_major_formatter(formatter)\n",
    "ax1.set_xlabel(xlbl)\n",
    "ax1.set_ylabel(ylbl)\n",
    "ax1.set_title(\"Electric field in the stopband of the filter\")\n",
    "sim1.plot_field(field_monitor_name=\"field\", field_name=\"Ez\", val=\"abs\", f=freq0, ax=ax2)\n",
    "# Update plot labels\n",
    "ax2.xaxis.set_major_formatter(formatter)\n",
    "ax2.yaxis.set_major_formatter(formatter)\n",
    "ax2.set_xlabel(xlbl)\n",
    "ax2.set_ylabel(ylbl)\n",
    "ax2.set_title(\"Electric field in the passband of the filter\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91894098",
   "metadata": {},
   "source": [
    "## Creating a Network Using the `scikit-rf` Package\n",
    "In order to help us validate the design, we use the open source `scikit-rf` package. By treating each coupled line segment as an independent transmission line [2], we can build a simple circuit model of the network by chaining the four segments using the ABCD matrix representation [2].\n",
    "\n",
    "Once ABCD matrices have been generated for each segment, it is quite simple to compute the scattering parameters using the functionality provided by `scikit-rf`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f4ff32fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Helper function that creates a model of the 2 port filter network using the scikit-rf package.\n",
    "# This model is useful for validation, but it is based on transmission line models.\n",
    "# As a result, it will not be as accurate as a converged full-wave solution.\n",
    "def create_network_model_four_segment_coupler_with_scikit(\n",
    "    freqs, lengths, widths, height, gaps, eps_sub\n",
    "):\n",
    "    # Helper function that creates an ABCD matrix for each coupler section.\n",
    "    def abcd_from_coupled_strip(lambda_0, length, width, height, gap, eps_sub):\n",
    "        # Use models provided in the microwave plugin to estimate characteristic impedance\n",
    "        # and effective permittivities for the even and odd modes of the coupled line\n",
    "        (z0_even, z0_odd, er_eff_even, er_eff_odd) = (\n",
    "            mw.models.coupled_microstrip.compute_line_params(\n",
    "                relative_permittivity=eps_sub, width=width, height=height, gap=gap\n",
    "            )\n",
    "        )\n",
    "        # First we estimate the electrical length of the segment\n",
    "        # Use the geometric mean of the even/odd permittivities as an approximation for the relative permittivity\n",
    "        er_geo_mean = np.sqrt(er_eff_even * er_eff_odd)\n",
    "        # Phase constant is computed using free space wavelength and the effective permittivity\n",
    "        beta = 2 * np.pi / lambda_0 * np.sqrt(er_geo_mean)\n",
    "        # Microstrips terminated by an open circuit have fringing fields that can be modeled as a slight extension to the microstrip.\n",
    "        # This functionality is available in the microwave plugin.\n",
    "        dL = mw.models.microstrip.compute_end_effect_length(eps_sub, er_geo_mean, width, height)\n",
    "        # With the modified length and phase constant the electrical length is estimated for this segment.\n",
    "        theta = beta * (length + dL)\n",
    "        costheta = np.cos(theta)\n",
    "        sintheta = np.sin(theta)\n",
    "        # Compute the characteristic impedance of the coupler line segment using Table 8.8 from [2]\n",
    "        impedance = np.sqrt(\n",
    "            ((z0_even - z0_odd) ** 2 - (z0_even + z0_odd) ** 2 * costheta**2).astype(complex)\n",
    "        ) / (2 * sintheta)\n",
    "        # Using Equation 8.103 from [2]\n",
    "        cosbetal = costheta * (z0_even + z0_odd) / (z0_even - z0_odd)\n",
    "        sinthetal = np.sqrt((1 - cosbetal**2).astype(complex))\n",
    "        # This is the canonical ABCD matrix for a transmission line\n",
    "        A = cosbetal\n",
    "        B = 1j * impedance * sinthetal\n",
    "        C = 1j / impedance * sinthetal\n",
    "        D = cosbetal\n",
    "        # Change location of frequency dimension to conform to the scikit-rf format\n",
    "        abcd = np.array([[A, B], [C, D]])\n",
    "        return np.transpose(abcd, axes=(2, 0, 1))\n",
    "\n",
    "    # Compute the wavelengths in free space\n",
    "    lambda_0 = 3e14 / freqs  # speed of light in microns/s\n",
    "    abcd1 = abcd_from_coupled_strip(\n",
    "        lambda_0,\n",
    "        length=lengths[0],\n",
    "        width=widths[0],\n",
    "        height=height,\n",
    "        gap=gaps[0],\n",
    "        eps_sub=eps_sub,\n",
    "    )\n",
    "    abcd2 = abcd_from_coupled_strip(\n",
    "        lambda_0,\n",
    "        length=lengths[1],\n",
    "        width=widths[1],\n",
    "        height=height,\n",
    "        gap=gaps[1],\n",
    "        eps_sub=eps_sub,\n",
    "    )\n",
    "\n",
    "    freq = rf.Frequency.from_f(freqs, unit=\"Hz\")\n",
    "    abcd1_ntw = rf.Network(frequency=freq, a=abcd1, name=\"cstrip1\")\n",
    "    abcd2_ntw = rf.Network(frequency=freq, a=abcd2, name=\"cstrip2\")\n",
    "\n",
    "    port1 = rf.Circuit.Port(freq, \"port1\")\n",
    "    port2 = rf.Circuit.Port(freq, \"port2\")\n",
    "\n",
    "    # The complete network is made up of four segments where there are only two unique segments\n",
    "    chain = abcd1_ntw**abcd2_ntw**abcd2_ntw**abcd1_ntw\n",
    "\n",
    "    # Add the ports to the network\n",
    "    cnx = [\n",
    "        [(port1, 0), (chain, 0)],\n",
    "        [(chain, 1), (port2, 0)],\n",
    "    ]\n",
    "    # building the circuit\n",
    "    cir = rf.Circuit(cnx)\n",
    "    # return the resulting Network from the 'network' parameter:\n",
    "    return cir.network"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "288fcdbb",
   "metadata": {},
   "source": [
    "## Plotting the Scattering Parameters from the Initial Design\n",
    "\n",
    "We may now plot the computed scattering parameters $S_{11}$ and $S_{12}$ using both the results from the `scikit-rf` model and the `Tidy3D` simulations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a7e3968f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1100x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create the network using transmission line models for comparison with tidy3D simulations\n",
    "ntw = create_network_model_four_segment_coupler_with_scikit(\n",
    "    freqs=freqs,\n",
    "    lengths=lengths,\n",
    "    widths=widths,\n",
    "    height=h_sub,\n",
    "    gaps=gaps,\n",
    "    eps_sub=eps_sub,\n",
    ")\n",
    "# Plot the scattering parameters related to reflection and transmission\n",
    "_, (ax1, ax2) = plt.subplots(1, 2, figsize=(11, 3))\n",
    "ax1.plot(\n",
    "    freqs / 1e9,\n",
    "    20 * np.log10(np.abs(ntw.s[:, 0, 0])),\n",
    "    \"-r\",\n",
    "    s_matrix.f / 1e9,\n",
    "    20 * np.log10(np.abs(s_matrix.isel(port_out=0, port_in=0).values.flatten())),\n",
    "    \"--b\",\n",
    ")\n",
    "ax1.legend([\"scikit-rf\", \"Tidy3D\"], loc=\"lower right\")\n",
    "ax1.set_xlabel(\"Frequency (GHz)\")\n",
    "ax1.set_ylabel(r\"$|S_{11}|$ (dB)\")\n",
    "ax1.set_ylim([-50, 1])\n",
    "ax1.grid()\n",
    "\n",
    "ax2.plot(\n",
    "    freqs / 1e9,\n",
    "    20 * np.log10(np.abs(ntw.s[:, 0, 1])),\n",
    "    \"-r\",\n",
    "    s_matrix.f / 1e9,\n",
    "    20 * np.log10(np.abs(s_matrix.isel(port_out=0, port_in=1).values.flatten())),\n",
    "    \"--b\",\n",
    ")\n",
    "ax2.legend([\"scikit-rf\", \"Tidy3D\"], loc=\"lower right\")\n",
    "ax2.set_xlabel(\"Frequency (GHz)\")\n",
    "ax2.set_ylabel(r\"$|S_{12}|$ (dB)\")\n",
    "ax2.set_ylim([-50, 1])\n",
    "ax2.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "640b8963",
   "metadata": {},
   "source": [
    "## Optimized Design of Bandpass Filter\n",
    "\n",
    "From the previous results we observe that the filter is not exactly centered at 6 GHz, which was a desired feature. In this part, we optimize the microstrip parameters from [1] to achieve a better implementation of the desired filter.\n",
    "\n",
    "The specified even and odd characteristic impedances for the coupled lines from [1] are tabulated below.\n",
    "\n",
    "| Segment  | $$Z_{\\textrm{even}} ~(\\Omega)$$ | $$Z_{\\textrm{odd}} ~(\\Omega)$$ |\n",
    "| -------- | ---------- | ------------ |\n",
    "| 1 |   101.5 | 38.5 |\n",
    "| 2 |   71 | 39 |\n",
    "| 3 |   71 | 39 |\n",
    "| 4 |   101.5 | 38.5 |\n",
    "\n",
    "In the `microwave` plugin, we have made available approximate models for a few common transmission line types. These models allow us to quickly compute the expected even and odd characteristic impedances for a segment of coupled microstrip lines with a given width and gap.\n",
    "\n",
    "We now use the `optimize` module from the `scipy` package to find better widths, gaps, and lengths. These optimized parameters will result in coupled microstrip lines that more closely match the desired even and odd characteristic impedances from [1]."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "da2bde72",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The new optimized dimensions for the coupled microstrips are: \n",
      "Widths (mm): [1.4161279  2.39856517 2.39856517 1.4161279 ]\n",
      "Lengths (mm): [6.60907322 6.39916412 6.39916412 6.60907322]\n",
      "Gaps (mm): [0.13409214 0.3863375  0.3863375  0.13409214]\n"
     ]
    }
   ],
   "source": [
    "# Given parameters are a list of desired even and odd impedances, from [1]\n",
    "Z_evens = [101.5, 71, 71, 101.5]\n",
    "Z_odds = [38.5, 39, 39, 38.5]\n",
    "\n",
    "\n",
    "# Define a target function that is used to find the optimal width and gap of a coupled microstrip given a desired even/odd impedance pair\n",
    "def target_func(x, *args):\n",
    "    width = x[0]\n",
    "    gap = x[1]\n",
    "    # Tidy3D microwave plugin function to quickly approximate parameters of coupled microstrips\n",
    "    (z0_even, z0_odd, _, _) = mw.models.coupled_microstrip.compute_line_params(\n",
    "        relative_permittivity=eps_sub, width=width, height=h_sub, gap=gap\n",
    "    )\n",
    "    even_target = args[0]\n",
    "    odd_target = args[1]\n",
    "    # Simple error metric\n",
    "    error = (z0_even - even_target) ** 2 + (z0_odd - odd_target) ** 2\n",
    "    return error\n",
    "\n",
    "\n",
    "# Use approximate models for microstrip parameters to compute the needed widths, gaps, and lengths of segments\n",
    "strips_W = []\n",
    "strips_G = []\n",
    "strips_L = []\n",
    "for Z_even, Z_odd in zip(Z_evens, Z_odds):\n",
    "    # Search for solutions within the domain 1 < width < 10 and 0.05 < gap < 1\n",
    "    bounds = [(1 * mm, 10 * mm), (0.05 * mm, 1 * mm)]\n",
    "    # Target values for even/odd impedances\n",
    "    args = (Z_even, Z_odd)\n",
    "    res = opt.minimize(\n",
    "        target_func,\n",
    "        x0=[5 * mm, 0.5 * mm],\n",
    "        bounds=bounds,\n",
    "        tol=1e-4,\n",
    "        # We use one of the suggested algorithms from SciPy for bound constrained optimization problems.\n",
    "        method=\"L-BFGS-B\",\n",
    "        args=args,\n",
    "    )\n",
    "    width = res.x[0]\n",
    "    gap = res.x[1]\n",
    "    strips_W.append(width)\n",
    "    strips_G.append(gap)\n",
    "    # Compute the required segment length which needs to have an electrical length of lambda/4\n",
    "    (_, _, er_eff_even, er_eff_odd) = mw.models.coupled_microstrip.compute_line_params(\n",
    "        relative_permittivity=eps_sub, width=width, height=h_sub, gap=gap\n",
    "    )\n",
    "    # Use the geometric mean of the even/odd permittivities as an approximation for the relative permittivity\n",
    "    er_geo_mean = np.sqrt(er_eff_even * er_eff_odd)\n",
    "    length = (lda0) / 4 / (np.sqrt(er_geo_mean))\n",
    "    # Microstrips terminated by an open circuit have fringing fields that can be modelled as a slight extension to the microstrip\n",
    "    dL = mw.models.microstrip.compute_end_effect_length(eps_sub, er_geo_mean, width, h_sub)\n",
    "    # As a result, we need a slightly shorter section\n",
    "    length -= dL\n",
    "    strips_L.append(length)\n",
    "\n",
    "# Numpy arrays of the microstrip dimensions\n",
    "lengths = np.array(strips_L)\n",
    "widths = np.array(strips_W)\n",
    "gaps = np.array(strips_G)\n",
    "\n",
    "print(\"The new optimized dimensions for the coupled microstrips are: \")\n",
    "print(f\"Widths (mm): {widths * 1e-3}\")\n",
    "print(f\"Lengths (mm): {lengths * 1e-3}\")\n",
    "print(f\"Gaps (mm): {gaps * 1e-3}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68f8ecd0",
   "metadata": {},
   "source": [
    "Using the previously defined helper functions, we can quickly set up the new structures and simulations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "659918cf",
   "metadata": {},
   "outputs": [
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       "\u001b[2;36m14:11:26 EDT\u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'smatrix_lumped_port_1'\u001b[0m with task_id                  \n",
       "\u001b[2;36m             \u001b[0m\u001b[32m'fdve-c2d09797-c68c-4ded-95c8-967b5a0b206e'\u001b[0m and task_type \u001b[32m'FDTD'\u001b[0m.  \n"
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       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-c2d09797-c68c-4ded-95c8-967b5a0b206e\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'https://tidy3d.simulation.cloud/workbench?taskId=fdve-c2d09797-c68</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-c2d09797-c68c-4ded-95c8-967b5a0b206e\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">c-4ded-95c8-967b5a0b206e'</span></a>.                                         \n",
       "</pre>\n"
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       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mView task using web UI at                                          \n",
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       "\u001b[2;36m             \u001b[0m\u001b]8;id=245085;https://tidy3d.simulation.cloud/workbench?taskId=fdve-c2d09797-c68c-4ded-95c8-967b5a0b206e\u001b\\\u001b[32mc-4ded-95c8-967b5a0b206e'\u001b[0m\u001b]8;;\u001b\\.                                         \n"
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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\">14:11:27 EDT </span>Created task <span style=\"color: #008000; text-decoration-color: #008000\">'smatrix_lumped_port_2'</span> with task_id                  \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'fdve-89fb21d0-4f37-4249-979f-03f9e14688ec'</span> and task_type <span style=\"color: #008000; text-decoration-color: #008000\">'FDTD'</span>.  \n",
       "</pre>\n"
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       "\u001b[2;36m14:11:27 EDT\u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'smatrix_lumped_port_2'\u001b[0m with task_id                  \n",
       "\u001b[2;36m             \u001b[0m\u001b[32m'fdve-89fb21d0-4f37-4249-979f-03f9e14688ec'\u001b[0m and task_type \u001b[32m'FDTD'\u001b[0m.  \n"
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       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-89fb21d0-4f37-4249-979f-03f9e14688ec\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'https://tidy3d.simulation.cloud/workbench?taskId=fdve-89fb21d0-4f3</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-89fb21d0-4f37-4249-979f-03f9e14688ec\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">7-4249-979f-03f9e14688ec'</span></a>.                                         \n",
       "</pre>\n"
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       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mView task using web UI at                                          \n",
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       "\u001b[2;36m             \u001b[0m\u001b]8;id=183883;https://tidy3d.simulation.cloud/workbench?taskId=fdve-89fb21d0-4f37-4249-979f-03f9e14688ec\u001b\\\u001b[32m7-4249-979f-03f9e14688ec'\u001b[0m\u001b]8;;\u001b\\.                                         \n"
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       "</pre>\n"
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       "\n"
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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\">14:11:29 EDT </span>Started working on Batch.                                          \n",
       "</pre>\n"
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       "\u001b[2;36m14:11:29 EDT\u001b[0m\u001b[2;36m \u001b[0mStarted working on Batch.                                          \n"
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       "</pre>\n"
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       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Batch has completed.                                               \n",
       "</pre>\n"
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       "\u001b[2;36m             \u001b[0mBatch has completed.                                               \n"
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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\">14:12:31 EDT </span>loading simulation from                                            \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>.<span style=\"color: #800080; text-decoration-color: #800080\">/</span><span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">fdve-c2d09797-c68c-4ded-95c8-967b5a0b206e.hdf5</span>                   \n",
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       "\u001b[2;36m14:12:31 EDT\u001b[0m\u001b[2;36m \u001b[0mloading simulation from                                            \n",
       "\u001b[2;36m             \u001b[0m.\u001b[35m/\u001b[0m\u001b[95mfdve-c2d09797-c68c-4ded-95c8-967b5a0b206e.hdf5\u001b[0m                   \n"
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       "model_id": "0a4c16aeaaea4fe6840c01766d218999",
       "version_major": 2,
       "version_minor": 0
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      "text/plain": [
       "Output()"
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     "output_type": "display_data"
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       "\n"
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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\">14:12:36 EDT </span>loading simulation from                                            \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>.<span style=\"color: #800080; text-decoration-color: #800080\">/</span><span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">fdve-89fb21d0-4f37-4249-979f-03f9e14688ec.hdf5</span>                   \n",
       "</pre>\n"
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       "\u001b[2;36m14:12:36 EDT\u001b[0m\u001b[2;36m \u001b[0mloading simulation from                                            \n",
       "\u001b[2;36m             \u001b[0m.\u001b[35m/\u001b[0m\u001b[95mfdve-89fb21d0-4f37-4249-979f-03f9e14688ec.hdf5\u001b[0m                   \n"
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   "source": [
    "# Create the structures and geometry of the coupled line bandpass filter\n",
    "(total_L, total_W, xstarts, ystarts) = get_coupled_line_geometry(lengths, widths, gaps)\n",
    "coupled_lines = generate_coupled_lines(lengths, widths, gaps, xstarts, ystarts)\n",
    "\n",
    "# Define the simulation domain size with some extra padding based on central wavelength\n",
    "Lx = total_L + lda0 / 5\n",
    "Ly = total_W + lda0 / 5\n",
    "Lz = h_sub + lda0 / 8\n",
    "\n",
    "# Definition of mesh overrides\n",
    "mesh_overrides = [\n",
    "    # The first mesh override ensures the small height of the strips is accurately modeled.\n",
    "    td.MeshOverrideStructure(\n",
    "        geometry=td.Box(\n",
    "            center=[0, 0, h_trace / 2],\n",
    "            size=[1.1 * total_L, 1.1 * total_W, h_trace],\n",
    "        ),\n",
    "        dl=[total_L / 200, np.min(widths) / 20, h_trace],\n",
    "    ),\n",
    "    # The second mesh override refined the grid within the substrate.\n",
    "    td.MeshOverrideStructure(\n",
    "        geometry=td.Box(\n",
    "            center=[0, 0, -h_sub / 2],\n",
    "            size=[1.1 * total_L, 1.1 * total_W, h_sub],\n",
    "        ),\n",
    "        dl=[total_L / 200, np.min(widths) / 20, h_sub / 20],\n",
    "    ),\n",
    "]\n",
    "\n",
    "# The base tidy3D Simulation ready to be used by the TerminalComponentModeler\n",
    "sim = td.Simulation(\n",
    "    center=(0, 0, Lz / 2 - h_sub),\n",
    "    size=(Lx, Ly, Lz),\n",
    "    grid_spec=td.GridSpec.auto(\n",
    "        min_steps_per_wvl=40.0,\n",
    "        wavelength=lda0,\n",
    "        dl_min=h_trace / 5,\n",
    "        override_structures=mesh_overrides,\n",
    "    ),\n",
    "    structures=[substrate] + coupled_lines,\n",
    "    sources=[],\n",
    "    monitors=[field_monitor],\n",
    "    run_time=10e-9,\n",
    "    boundary_spec=boundary_spec,\n",
    ")\n",
    "\n",
    "# Compute port xy locations\n",
    "port_left = (xstarts[0], ystarts[0] - widths[0] / 2)\n",
    "port_right = (xstarts[-1] + lengths[-1], ystarts[-1] - gaps[-1] - 1.5 * widths[-1])\n",
    "# Impedance at each port\n",
    "reference_impedance = 50\n",
    "port_1_td = smatrix.LumpedPort(\n",
    "    center=(port_left[0], port_left[1], -h_sub / 2),\n",
    "    size=(0, widths[0], h_sub),\n",
    "    voltage_axis=2,\n",
    "    name=\"lumped_port_1\",\n",
    "    impedance=reference_impedance,\n",
    ")\n",
    "\n",
    "port_2_td = smatrix.LumpedPort(\n",
    "    center=(port_right[0], port_right[1], -h_sub / 2),\n",
    "    size=(0, widths[-1], h_sub),\n",
    "    voltage_axis=2,\n",
    "    name=\"lumped_port_2\",\n",
    "    impedance=reference_impedance,\n",
    ")\n",
    "\n",
    "modeler = smatrix.TerminalComponentModeler(\n",
    "    simulation=sim,\n",
    "    ports=[port_1_td, port_2_td],\n",
    "    freqs=freqs,\n",
    "    verbose=True,\n",
    ")\n",
    "\n",
    "# Run the jobs and save the scattering parameters\n",
    "s_matrix = modeler.run()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c4b0c47e",
   "metadata": {},
   "source": [
    "### Plotting the Scattering Parameters from the Optimized Design\n",
    "\n",
    "We again create a transmission line model of the network using `scikit-rf` and the helper function `create_network_model_four_segment_coupler_with_scikit`. We plot the scattering parameters for the optimized design and compare to those computed by the `scikit-rf` model.\n",
    "\n",
    "The optimized filter is now centered at 6 GHz. In addition, the return loss has increased from about 15 dB to approximately 30 dB around the central frequency, which indicates much less reflected power."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "8891493d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1100x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create the network using transmission line models for comparison with tidy3D simulations\n",
    "ntw = create_network_model_four_segment_coupler_with_scikit(\n",
    "    freqs=freqs,\n",
    "    lengths=lengths,\n",
    "    widths=widths,\n",
    "    height=h_sub,\n",
    "    gaps=gaps,\n",
    "    eps_sub=eps_sub,\n",
    ")\n",
    "\n",
    "# Plot the scattering parameters related to reflection and transmission\n",
    "_, (ax1, ax2) = plt.subplots(1, 2, figsize=(11, 3))\n",
    "ax1.plot(\n",
    "    freqs / 1e9,\n",
    "    20 * np.log10(np.abs(ntw.s[:, 0, 0])),\n",
    "    \"-r\",\n",
    "    s_matrix.f / 1e9,\n",
    "    20 * np.log10(np.abs(s_matrix.isel(port_out=0, port_in=0).values.flatten())),\n",
    "    \"--b\",\n",
    ")\n",
    "ax1.legend([\"scikit-rf\", \"Tidy3D\"], loc=\"lower right\")\n",
    "ax1.set_xlabel(\"Frequency (GHz)\")\n",
    "ax1.set_ylabel(r\"$|S_{11}|$ (dB)\")\n",
    "ax1.set_ylim([-50, 1])\n",
    "ax1.grid()\n",
    "\n",
    "ax2.plot(\n",
    "    freqs / 1e9,\n",
    "    20 * np.log10(np.abs(ntw.s[:, 0, 1])),\n",
    "    \"-r\",\n",
    "    s_matrix.f / 1e9,\n",
    "    20 * np.log10(np.abs(s_matrix.isel(port_out=0, port_in=1).values.flatten())),\n",
    "    \"--b\",\n",
    ")\n",
    "ax2.legend([\"scikit-rf\", \"Tidy3D\"], loc=\"upper right\")\n",
    "ax2.set_xlabel(\"Frequency (GHz)\")\n",
    "ax2.set_ylabel(r\"$|S_{12}|$ (dB)\")\n",
    "ax2.set_ylim([-50, 1])\n",
    "ax2.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49ce7b9a",
   "metadata": {},
   "source": [
    "## References\n",
    "\n",
    "[1]   Taoufik, Ragani, N. Amar Touhami, and M. Agoutane. \"Designing a Microstrip coupled line bandpass filter.\"\n",
    "      International Journal of Engineering & Technology 2, no. 4 (2013): 266.\n",
    "\n",
    "[2]   David M. Pozar, \"Microwave Filters\" in Microwave Engineering, 4th ed. 2011, ch. 8."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7470f8d5-1b7a-45e3-b9ab-d0c99339655f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "applications": [
   "Microwave and RF devices"
  ],
  "description": "Microwave filters serve as essential components in microwave and radio frequency communication systems. These critical building blocks help us exert control over the frequencies that are transmitted through a system. Implementing microwave filters can be achieved through various methods, for example, utilizing lumped elements, sections of transmission lines, or a combination of both. In this notebook, we will guide you through the process of designing and validating a bandpass filter using multiple sections of coupled microstrip lines.",
  "feature_image": "./img/coupled_line_bandpass_filter.png",
  "features": [
   "Lumped port",
   "Smatrix plugin"
  ],
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "keywords": "microwave, RF, network analysis, insertion loss method, filter, Tidy3D, FDTD",
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.7"
  },
  "title": "Designing and optimizing a coupled microstrip line bandpass filter in Tidy3D FDTD"
 },
 "nbformat": 4,
 "nbformat_minor": 5
}