10-Simphony_MZM_Simulation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Using S-Parameters to simulate whole circuits\n",
    "\n",
    "It is computationally expensive to simulate every device in a circuit simultaneously using MEEP or other FDTD methods. Instead, we can simulate each individual component once in order to generate a table of s-parameters and their corresponding frequencies. With the frequency-dependent s-parameters known for each element in a photonic circuit, we can cascade them together to determine the steady-state behavior of the circuit as a whole.\n",
    "\n",
    "The s-parameters for each device form a matrix of complex numbers (phase and magnitude), with one column per input port and one row per output port. Consecutive photonic circuit elements are cascaded by multiplying the corresponding matrices together:\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Maxwell's Equations\n",
    "\n",
    "Example s-parameter calculation with a 3-component photonic circuit with two external inputs and two external outputs:\n",
    "\n",
    "$$\\left(\\begin{matrix}\n",
    "out_1 \\\\ out_2\n",
    "\\end{matrix}\\right)\n",
    "=\n",
    "\\left(\\begin{matrix}\n",
    "A_{11} & A_{12} & A_{13}\\\\\n",
    "A_{21} & A_{22} & A_{23}\\\\\n",
    "\\end{matrix}\\right)\n",
    "\\left(\\begin{matrix}\n",
    "B_{11}\\\\\n",
    "B_{21}\\\\\n",
    "B_{31}\\\\\n",
    "\\end{matrix}\\right)\n",
    "\\left(\\begin{matrix}\n",
    "C_{11} & C_{12}\\\\\n",
    "\\end{matrix}\\right)\n",
    "\\left(\\begin{matrix}\n",
    "in_1 \\\\ in_2\\\\\n",
    "\\end{matrix}\\right)\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "# Simphony\n",
    "Simphony is a python module designed to automate the process of running the s-parameter cascade, with a set of basic photonic building-blocks already included and efficient calculation of frequency-dependent s-parameters for these blocks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Library containing models of basic components\n",
    "from simphony.library import siepic           \n",
    "\n",
    "#Class representing a photonic circuit\n",
    "from simphony.netlist import Subcircuit\n",
    "\n",
    "#Class representing a photonic circuit frequency sweep simulation\n",
    "from simphony.simulation import SweepSimulation\n",
    "\n",
    "#Helper function\n",
    "from simphony.tools import freq2wl\n",
    "\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Creating a Photonic Circuit\n",
    "\n",
    "A circuit (Subcircuit) is comprised of Elements. Each element is an instance of a Model. Multiple identical elements may be instantiated from the same Model\n",
    "\n",
    "Circuit design approach:\n",
    " - Define each unique Model used\n",
    " - Create Subcircuit\n",
    " - Used Subcircuit.add to automatically instantiate elements from the predefined models\n",
    " \n",
    "Once a circuit is designed, it may be simulated by specifying frequency start and end points"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Example: Mach-Zehnder Modulator\n",
    " - Grating coupler on either side\n",
    " - Two couplers with different-lengthed waveguides between them"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Define all photonic elements using basic library components\n",
    "#All components are optimized for 1550 nm TE light\n",
    "gratingCoupler = siepic.ebeam_gc_te1550()\n",
    "longWaveguide  = siepic.ebeam_wg_integral_1550(length=200e-6)\n",
    "shortWaveguide = siepic.ebeam_wg_integral_1550(length=100e-6)\n",
    "coupler        = siepic.ebeam_bdc_te1550() #gap = 2e-7, length=10e-6\n",
    "terminator     = siepic.ebeam_terminator_te1550()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Create the circuit and give it a name\n",
    "circuit = Subcircuit(\"MZM\")\n",
    "\n",
    "#Create each Element from its Model, giving each one a UNIQUE name\n",
    "e = circuit.add([\n",
    "    (gratingCoupler,    \"input\"),\n",
    "    (       coupler, \"splitter\"),\n",
    "    ( longWaveguide,     \"long\"),\n",
    "    (shortWaveguide,    \"short\"),\n",
    "    (       coupler, \"combiner\"),\n",
    "    (gratingCoupler,   \"output\"),\n",
    "])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pins\n",
    "\n",
    "Each port of a circuit element is called a pin in Simphony and assigned a name. Pins may be connected together within a subcircuit, and any disconnected pins are available externally as overall inputs/outputs. The default pin names may be found in the documentation at:\n",
    "\n",
    "https://simphonyphotonics.readthedocs.io/en/stable/index.html\n",
    "\n",
    "They are typically of the form \"n1\", \"n2\", \"n3\", etc.\n",
    "\n",
    "It is helpful to assign externally accessibly pins you want to access unique names. The \"n2\" pins of the grating couplers represent their external connections."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "circuit.elements[\"input\"].pins[\"n2\"] = \"input\"\n",
    "circuit.elements[\"output\"].pins[\"n2\"] = \"output\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "While it is not required, your code can be more sensible if you also assign the pin names of some of the internal elements. These may all be assigned at once:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "circuit.elements['splitter'].pins = (\"mainInput\", \"secInput\",\n",
    "                                    \"output1\", \"output2\")\n",
    "circuit.elements['combiner'].pins = (\"input1\", \"input2\",\n",
    "                                    \"mainOutput\", \"secOutput\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Connections\n",
    "Each pin may be connected to one other pin using circuit.connect or circuit.connect_many. Either function accepts a desired connection in the form of a tuple of length 4:\n",
    "\n",
    "(first element name, first element port, connecting element name, connecting element port)\n",
    "\n",
    "The order does not matter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "circuit.connect_many([\n",
    "    (   \"input\",         \"n1\", \"splitter\", \"mainInput\"),\n",
    "    (\"splitter\",    \"output1\",     \"long\",        \"n1\"),\n",
    "    (\"splitter\",    \"output2\",    \"short\",        \"n1\"),\n",
    "    (    \"long\",         \"n2\", \"combiner\",    \"input1\"),\n",
    "    (   \"short\",         \"n2\", \"combiner\",    \"input2\"),\n",
    "    (\"combiner\", \"mainOutput\",   \"output\",        \"n1\"),\n",
    "])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Simulation\n",
    "\n",
    "With the circuit fully specified, we can now simulate it. We use a SweepSimulation that calculates the input/output relationship for a series of linearly spaced wavelengths:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "simulation = SweepSimulation(circuit, 1500e-9, 1600e-9)\n",
    "result     = simulation.simulate()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Extract and Plot Data\n",
    "\n",
    "We can extract the data from the result object using the pin names and plot it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "f, s = result.data(\"input\", \"output\")\n",
    "w = freq2wl(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Output Magnitude Proportion')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(w*10**9,s)\n",
    "plt.title(\"MZM\")\n",
    "plt.xlabel(\"Wavelengths (nm)\")\n",
    "plt.ylabel(\"Output Magnitude Proportion\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}