{
 "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": 27,
   "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  # For validation using circuit models of the filter\n",
    "import tidy3d as td\n",
    "import tidy3d.rf as rf\n",
    "import tidy3d.web as web\n",
    "from scipy import (\n",
    "    optimize as opt,\n",
    ")\n",
    "\n",
    "# Tidy3d plugin imports\n",
    "from tidy3d.plugins import microwave as mw\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": 28,
   "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": 29,
   "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": 30,
   "id": "882e2718",
   "metadata": {},
   "outputs": [
    {
     "data": {
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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": 31,
   "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": 32,
   "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": 33,
   "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 = rf.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 = rf.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 = rf.TerminalComponentModeler(\n",
    "    simulation=sim,\n",
    "    ports=[port_1_td, port_2_td],\n",
    "    freqs=freqs,\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": 34,
   "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\">14:05:48 EST </span>Created task <span style=\"color: #008000; text-decoration-color: #008000\">'coupled_line_bandpass_filter'</span> with resource_id       \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'sid-4ccd7f10-ac61-44f8-9f68-a3e83e730fb3'</span> and task_type           \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'TERMINAL_CM'</span>.                                                     \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:05:48 EST\u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'coupled_line_bandpass_filter'\u001b[0m with resource_id       \n",
       "\u001b[2;36m             \u001b[0m\u001b[32m'sid-4ccd7f10-ac61-44f8-9f68-a3e83e730fb3'\u001b[0m and task_type           \n",
       "\u001b[2;36m             \u001b[0m\u001b[32m'TERMINAL_CM'\u001b[0m.                                                     \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>View task using web UI at                                          \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/rf?taskId=pa-e37cd330-be3c-463c-878a-e51050403e9d\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'https://tidy3d.simulation.cloud/rf?taskId=pa-e37cd330-be3c-463c-87</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/rf?taskId=pa-e37cd330-be3c-463c-878a-e51050403e9d\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">8a-e51050403e9d'</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=133405;https://tidy3d.simulation.cloud/rf?taskId=pa-e37cd330-be3c-463c-878a-e51050403e9d\u001b\\\u001b[32m'https://tidy3d.simulation.cloud/rf?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=313214;https://tidy3d.simulation.cloud/rf?taskId=pa-e37cd330-be3c-463c-878a-e51050403e9d\u001b\\\u001b[32mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=133405;https://tidy3d.simulation.cloud/rf?taskId=pa-e37cd330-be3c-463c-878a-e51050403e9d\u001b\\\u001b[32m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=749577;https://tidy3d.simulation.cloud/rf?taskId=pa-e37cd330-be3c-463c-878a-e51050403e9d\u001b\\\u001b[32mpa\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=133405;https://tidy3d.simulation.cloud/rf?taskId=pa-e37cd330-be3c-463c-878a-e51050403e9d\u001b\\\u001b[32m-e37cd330-be3c-463c-87\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=133405;https://tidy3d.simulation.cloud/rf?taskId=pa-e37cd330-be3c-463c-878a-e51050403e9d\u001b\\\u001b[32m8a-e51050403e9d'\u001b[0m\u001b]8;;\u001b\\.                                                  \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Task folder: <a href=\"https://tidy3d.simulation.cloud/folders/f89aec3e-3357-4624-9c24-096a87582f12\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'default'</span></a>.                                            \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mTask folder: \u001b]8;id=62308;https://tidy3d.simulation.cloud/folders/f89aec3e-3357-4624-9c24-096a87582f12\u001b\\\u001b[32m'default'\u001b[0m\u001b]8;;\u001b\\.                                            \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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      "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"
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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\">14:05:56 EST </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.304</span>. Minimum cost depends on task       \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>execution details. Use <span style=\"color: #008000; text-decoration-color: #008000\">'web.real_cost(task_id)'</span> after run.         \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:05:56 EST\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m0.304\u001b[0m. Minimum cost depends on task       \n",
       "\u001b[2;36m             \u001b[0mexecution details. Use \u001b[32m'web.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m after run.         \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Subtasks status - coupled_line_bandpass_filter                     \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Group ID: <span style=\"color: #008000; text-decoration-color: #008000\">'pa-e37cd330-be3c-463c-878a-e51050403e9d'</span>                \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mSubtasks status - coupled_line_bandpass_filter                     \n",
       "\u001b[2;36m             \u001b[0mGroup ID: \u001b[32m'pa-e37cd330-be3c-463c-878a-e51050403e9d'\u001b[0m                \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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      "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\">             </span>Batch status = preprocess                                          \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mBatch status = preprocess                                          \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">14:06:03 EST </span>Batch status = running                                             \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:06:03 EST\u001b[0m\u001b[2;36m \u001b[0mBatch status = running                                             \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\">14:06:37 EST </span>Batch status = postprocess                                         \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:06:37 EST\u001b[0m\u001b[2;36m \u001b[0mBatch status = postprocess                                         \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\">14:06:50 EST </span>Modeler has finished running successfully.                         \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:06:50 EST\u001b[0m\u001b[2;36m \u001b[0mModeler has finished running successfully.                         \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\">14:06:51 EST </span>Billed flex credit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.085</span>.                                    \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:06:51 EST\u001b[0m\u001b[2;36m \u001b[0mBilled flex credit cost: \u001b[1;36m0.085\u001b[0m.                                    \n"
      ]
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     "metadata": {},
     "output_type": "display_data"
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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>\n"
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     },
     "metadata": {},
     "output_type": "display_data"
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      "text/plain": [
       "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"
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      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">14:06:53 EST </span>Loading component modeler data from cm_data.hdf5                   \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:06:53 EST\u001b[0m\u001b[2;36m \u001b[0mLoading component modeler data from cm_data.hdf5                   \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Run the jobs and save the scattering parameters\n",
    "modeler_data = web.run(modeler, task_name=\"coupled_line_bandpass_filter\")\n",
    "s_matrix = modeler_data.smatrix()"
   ]
  },
  {
   "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": 35,
   "id": "3a41d1e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1100x300 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sim1 = modeler_data.data[\"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": 36,
   "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 = skrf.Frequency.from_f(freqs, unit=\"Hz\")\n",
    "    abcd1_ntw = skrf.Network(frequency=freq, a=abcd1, name=\"cstrip1\")\n",
    "    abcd2_ntw = skrf.Network(frequency=freq, a=abcd2, name=\"cstrip2\")\n",
    "\n",
    "    port1 = skrf.Circuit.Port(freq, \"port1\")\n",
    "    port2 = skrf.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 = skrf.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": 37,
   "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.data.f / 1e9,\n",
    "    20 * np.log10(np.abs(s_matrix.data.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.data.f / 1e9,\n",
    "    20 * np.log10(np.abs(s_matrix.data.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": 38,
   "id": "da2bde72",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The new optimized dimensions for the coupled microstrips are: \n",
      "Widths (mm): [1.41612799 2.39856519 2.39856519 1.41612799]\n",
      "Lengths (mm): [6.60907321 6.39916412 6.39916412 6.60907321]\n",
      "Gaps (mm): [0.13409209 0.38633752 0.38633752 0.13409209]\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": 39,
   "id": "659918cf",
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       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/rf?taskId=pa-c6bd72e1-0444-4431-8ec6-07962dbd2420\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'https://tidy3d.simulation.cloud/rf?taskId=pa-c6bd72e1-0444-4431-8e</span></a>\n",
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       "\u001b[2;36m             \u001b[0m\u001b]8;id=593815;https://tidy3d.simulation.cloud/rf?taskId=pa-c6bd72e1-0444-4431-8ec6-07962dbd2420\u001b\\\u001b[32mc6-07962dbd2420'\u001b[0m\u001b]8;;\u001b\\.                                                  \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 = rf.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 = rf.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 = rf.TerminalComponentModeler(\n",
    "    simulation=sim,\n",
    "    ports=[port_1_td, port_2_td],\n",
    "    freqs=freqs,\n",
    ")\n",
    "\n",
    "# Run the jobs and save the scattering parameters\n",
    "modeler_data = web.run(modeler, task_name=\"coupled_line_bandpass_filter_optimized\")\n",
    "s_matrix = modeler_data.smatrix()"
   ]
  },
  {
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   "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": 40,
   "id": "8891493d",
   "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",
    "\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.data.f / 1e9,\n",
    "    20 * np.log10(np.abs(s_matrix.data.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.data.f / 1e9,\n",
    "    20 * np.log10(np.abs(s_matrix.data.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."
   ]
  }
 ],
 "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": "3.13.7"
  },
  "title": "Designing and optimizing a coupled microstrip line bandpass filter in Tidy3D FDTD"
 },
 "nbformat": 4,
 "nbformat_minor": 5
}