diff --git a/_downloads/1d889b31bb06cb578abde8e2253d043a/setup.sh b/_downloads/1d889b31bb06cb578abde8e2253d043a/setup.sh deleted file mode 100644 index ffa5fa3..0000000 --- a/_downloads/1d889b31bb06cb578abde8e2253d043a/setup.sh +++ /dev/null @@ -1,18 +0,0 @@ -#!/bin/bash - -# tput setaf 2 - -wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -sh ./Miniconda3-latest-Linux-x86_64.sh -b -eval "$(~/miniconda3/bin/conda shell.bash hook)" -conda init -conda create --name photonics python=3.10 -y -conda activate photonics -pip install gdsfactory[full,gmsh,tidy3d,devsim,meow,sax,database,femwell,ray] -gf install klayout-integration -pip install jaxlib -conda install -c conda-forge pymeep=*=mpi_mpich_* nlopt -y -conda install -c conda-forge slepc4py=*=complex* -y -pip install simphony sipann -wget https://www.klayout.org/downloads/Ubuntu-22/klayout_0.28.7-1_amd64.deb -sudo dpkg -i klayout_0.28.7-1_amd64.deb diff --git a/_images/2617c9be4588c1a773558518893427243d24344ab6485bbac35367c16829ea95.png b/_images/2617c9be4588c1a773558518893427243d24344ab6485bbac35367c16829ea95.png deleted file mode 100644 index 29709ef..0000000 Binary files a/_images/2617c9be4588c1a773558518893427243d24344ab6485bbac35367c16829ea95.png and /dev/null differ diff --git a/_images/6a96f2438487b23bb76b90d9fd779d993f56718e8dbb496bd91079fe9dc5430f.png b/_images/6a96f2438487b23bb76b90d9fd779d993f56718e8dbb496bd91079fe9dc5430f.png new file mode 100644 index 0000000..a5d1777 Binary files /dev/null and b/_images/6a96f2438487b23bb76b90d9fd779d993f56718e8dbb496bd91079fe9dc5430f.png differ diff --git a/_images/9a415e85d0c43a7a40bed466a818eb08f4cae62e6a4b0a95e4be1e379298b378.png b/_images/9a415e85d0c43a7a40bed466a818eb08f4cae62e6a4b0a95e4be1e379298b378.png new file mode 100644 index 0000000..2259d12 Binary files /dev/null and b/_images/9a415e85d0c43a7a40bed466a818eb08f4cae62e6a4b0a95e4be1e379298b378.png differ diff --git a/_images/a220f3086fc5a316cfe8553831a6dbcaa06a392087595b44af8ee3e6812387d5.png b/_images/a220f3086fc5a316cfe8553831a6dbcaa06a392087595b44af8ee3e6812387d5.png deleted file mode 100644 index 121d4b1..0000000 Binary files a/_images/a220f3086fc5a316cfe8553831a6dbcaa06a392087595b44af8ee3e6812387d5.png and /dev/null differ diff --git a/_images/a9f4c8600d7499dc21fdcdccbf5f2b9f6184535efbd43c96489bc58f87d2e5c0.png b/_images/a9f4c8600d7499dc21fdcdccbf5f2b9f6184535efbd43c96489bc58f87d2e5c0.png deleted file mode 100644 index 0398671..0000000 Binary files a/_images/a9f4c8600d7499dc21fdcdccbf5f2b9f6184535efbd43c96489bc58f87d2e5c0.png and /dev/null differ diff --git a/_images/d30d68ddd43e49e0882b36f1269edec6dc7dbfabcadf0dd3dea898b99afe6fe2.png b/_images/d30d68ddd43e49e0882b36f1269edec6dc7dbfabcadf0dd3dea898b99afe6fe2.png new file mode 100644 index 0000000..43c706e Binary files /dev/null and b/_images/d30d68ddd43e49e0882b36f1269edec6dc7dbfabcadf0dd3dea898b99afe6fe2.png differ diff --git a/_sources/pages/git_and_github.md b/_sources/pages/git_and_github.md index f3ff01a..78b1b2a 100644 --- a/_sources/pages/git_and_github.md +++ b/_sources/pages/git_and_github.md @@ -20,7 +20,7 @@ To [install git](https://git-scm.com/book/en/v2/Getting-Started-Installing-Git) While you can install [Git for Windows](https://gitforwindows.org/), because the other software packages used in this course are Mac- or Linux-only, you -will be forced to use WSL to complete this course on Windows. Still, we'll +will be forced to use git via {term}`WSL` to complete this course. Still, we'll provide a [download link](https://git-scm.com/download/win) for git on Windows. ::: @@ -64,9 +64,9 @@ sudo dnf install git-all :::: -GitHub is the most well known hosting service, and it provides free accounts -(and free private repositories) to all users. This bootcamp, for example, is -hosted on GitHub, along with many of the most popular open-source Python -projects (including numpy, scipy, and matplotlib). If you want to version -control your code, we recommend creating an account on GitHub and keeping your -source code there. +[GitHub](https://github.com/) is the most well known hosting service, and it +provides free accounts (and free private repositories) to all users. This +bootcamp, for example, is hosted on GitHub, along with many of the most popular +open-source Python projects (including numpy, scipy, and matplotlib). If you +want to version control your code, we recommend creating an account on GitHub +and keeping your source code there. diff --git a/_sources/pages/glossary.md b/_sources/pages/glossary.md index 6ccbe06..3fe3c69 100644 --- a/_sources/pages/glossary.md +++ b/_sources/pages/glossary.md @@ -64,6 +64,9 @@ silicon-on-insulator SOI See {term}`silicon-on-insulator`. +vscode + Visual Studio Code, also commonly referred to as VS Code, is a source-code editor developed by Microsoft for Windows, Linux and macOS. Features include support for debugging, syntax highlighting, intelligent code completion, snippets, code refactoring, and embedded Git ([Wikipedia](https://en.wikipedia.org/wiki/Visual_Studio_Code)). + WSL Windows Subsystem for Linux. WSL lets developers install and run a Linux distribution on Windows and use Linux applications, utilities, and Bash command-line tools directly on Windows, unmodified, without the overhead of a traditional virtual machine or dual-boot setup. diff --git a/_sources/pages/klayout.md b/_sources/pages/klayout.md index 4c458f9..41dfdaa 100644 --- a/_sources/pages/klayout.md +++ b/_sources/pages/klayout.md @@ -7,3 +7,16 @@ [KLayout](https://www.klayout.de/) is a free and open-source software for layout design and verification. It's most basic use case is as a layout viewer (it can read and display GDS files, the most common format for laying out photonic chips), but it is a powerful tool for designing photonic devices and integrated circuits as well. It has features for {term}`DRC`, viewing chip cross-sections, tracing nets (to help you detect shorts), and more, while also being scriptable in several languages including Ruby and Python. KLayout is available for Windows, Mac, and Linux. You can download KLayout [here](https://www.klayout.de/build.html). + +## klive + +klive is a small extension to KLayout that allows automatic loading for GDS +files when some external program sends a json request with the gds path to a +klive server, running in the background. This essentially allows for +"hot-reloading" of your layouts within KLayout each time you rerun your code. + +Once KLayout is installed, you can install klive from within KLayout's package +manager, by going to `Tools > Manage Packages > Install New Packages`. Then, +search for `klive` and double click it to select it, then click "Apply". + +![klive installation screenshot](../klive_installation.png) diff --git a/_sources/pages/mzi.ipynb b/_sources/pages/mzi.ipynb index 923f78f..21f5d97 100644 --- a/_sources/pages/mzi.ipynb +++ b/_sources/pages/mzi.ipynb @@ -1,355 +1,541 @@ { "cells": [ { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "# Mach-Zehnder interferometers\n", "\n", - "The basic concept behind a Mach-Zehnder interferometer is that it splits a beam of light into two path and then recombines them. The resulting interference pattern depends on the relative phase of the two paths. This is a very common device in quantum optics, and is used in many experiments.\n", + "*This tutorial is an extract from the Simphony docs. For further background reading, see the [Introduction to simphony](https://simphonyphotonics.readthedocs.io/en/latest/tutorials/intro.html)*\n", "\n", - "Phase shifts may be caused by differing path lengths, or by the application of a phase shifter, such as a piezo-electric transducer." + "In this tutorial, we'll define and simulate a simple circuit known as a\n", + "Mach-Zender Interferometer (MZI).\n", + "\n", + "The basic concept behind a Mach-Zehnder interferometer is that it splits a beam of light into two paths and then later recombines them. The resulting interference pattern depends on the relative path length, and therefore the accumulated phase, between the two paths. This is a very common device in quantum optics, and is used in many experiments.\n", + "\n", + "Phase shifts may be caused by differing path lengths, or by the application of a phase shifter, such as a piezo-electric transducer.\n", + "\n", + "Simphony uses [SAX](https://flaport.github.io/sax/index.html) to define \n", + "models and simulate circuits, which uses \n", + "[JAX](https://jax.readthedocs.io/en/latest/index.html) as a computational \n", + "engine. JAX can provide a nice speedup for larger circuits if their models are \n", + "appropriately defined and you have a GPU. Otherwise, it will run perfectly fine \n", + "on a CPU." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Designing a Mach-Zehnder interferometer\n", - "![Image of a MZI](https://raw.githubusercontent.com/BYUCamachoLab/Photonics-Bootcamp/main/book/images/Notebook_Images/mzi_outline_ports_marked.webp)\n", - "\n", - "The above image shows a Mach-Zehnder interferometer. The input port is the top grating coupler and the output port is the bottom grating coupler. This particular design utilizes a path length difference to introduce a relative phase difference between the two paths. \n", - "\n", - "Simphony is an opensource photonic circuit simulator that already has a grating coupler model, y-branches, and waveguides which can be used to implement a Mach-Zehnder interferometer. The following code block was taken from the Simphony github and shows how to create a Mach-Zehnder interferometer using Simphony. To learn how to set up your own simulations in Simphony, see the official [Simphony documentation](https://simphonyphotonics.readthedocs.io/en/latest/index.html) " + ":::{note}\n", + "We run the following command **first** to ensure that JAX, the library that allows Simphony and SAX to run calculations on GPU's, uses [double precision](https://jax.readthedocs.io/en/latest/notebooks/Common_Gotchas_in_JAX.html#double-64bit-precision). Be aware that this setting must be set before JAX initializes, or the setting won't take.\n", + ":::" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 1, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "# Copyright © Simphony Project Contributors\n", - "# Licensed under the terms of the MIT License\n", - "# (see simphony/__init__.py for details)\n", - "\n", + "from jax import config\n", + "config.update(\"jax_enable_x64\", True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this tutorial, we'll use JAX as a drop in replacement for NumPy, matplotlib for visualizing our results, and SAX for constructing our circuit and running our simulations." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import jax.numpy as jnp\n", "import matplotlib.pyplot as plt\n", + "import sax" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## The MZI from it's components" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In an MZI, light entering the circuit is split and travels down two paths of differing lengths. When the light is recombined, it interferes, and the output is frequency-dependent." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "```{eval-rst}\n", + ".. figure:: /_static/images/mzi.png\n", + " :alt: Mach-Zehnder Interferometer (MZI) layout.\n", + " :align: center\n", "\n", - "from simphony.libraries import siepic\n", - "from simphony.simulation import Detector, Laser, Simulation\n", - "\n", - "# first we initialize all of the components in the MZI circuit\n", - "gc_input = siepic.GratingCoupler()\n", - "y_splitter = siepic.YBranch()\n", - "wg_long = siepic.Waveguide(length=150e-6)\n", - "wg_short = siepic.Waveguide(length=50e-6)\n", - "y_recombiner = siepic.YBranch()\n", - "gc_output = siepic.GratingCoupler()\n", - "\n", - "# next we connect the components to each other\n", - "# you can connect pins directly:\n", - "y_splitter[\"pin1\"].connect(gc_input[\"pin1\"])\n", - "\n", - "# or connect components with components:\n", - "# (when using components to make connections, their first unconnected pin will\n", - "# be used to make the connection.)\n", - "y_splitter.connect(wg_long)\n", - "\n", - "# or any combination of the two:\n", - "y_splitter[\"pin3\"].connect(wg_short)\n", - "# y_splitter.connect(wg_short[\"pin1\"])\n", - "\n", - "# when making multiple connections, it is often simpler to use `multiconnect`\n", - "# multiconnect accepts components, pins, and None\n", - "# if None is passed in, the corresponding pin is skipped\n", - "y_recombiner.multiconnect(gc_output, wg_short, wg_long)\n", - "\n", - "# do a simple sweep simulation\n", - "theoretical = None\n", - "with Simulation() as sim:\n", - " l = Laser(power=20e-3)\n", - " l.wlsweep(1500e-9, 1600e-9)\n", - " l.connect(gc_input)\n", - " Detector().connect(gc_output)\n", - "\n", - " theoretical = sim.sample()\n", - "\n", - "plt.plot(sim.freqs, theoretical[:, 0, 0])\n", - "plt.title(\"MZI\")\n", - "plt.tight_layout()\n", - "plt.show()\n", - "\n", - "# if we specify multiple samples, noise gets added to the simulation\n", - "with Simulation() as sim:\n", - " l = Laser(power=20e-3)\n", - " l.wlsweep(1500e-9, 1600e-9)\n", - " l.connect(gc_input)\n", - " Detector().connect(gc_output)\n", + " Chip layout of a Mach-Zehnder Interferometer (MZI).\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The MZI we’ll create can be broken down into constituent parts. Simphony includes models for these building blocks below:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "```{eval-rst}\n", + ".. figure:: /_static/images/mzi_outline_ports_marked.png\n", + " :alt: Mach-Zehnder Interferometer (MZI) block diagram\n", + " :align: center\n", "\n", - " # we get 101 samples even though we only use 3 because\n", - " # filtering requires at least 21 samples and the results\n", - " # get better with more samples and 101 isn't much slower\n", - " # than 21\n", - " noisy = sim.sample(101)" + " Block diagram of a Mach-Zehnder Interferometer (MZI) with the components labeled.\n", + "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "As you can see, and as you might have predicted, by varying the wavelength we can see different levels of power being output from the device. Notice how the power periodically dips to 0. This occurs when the phases of the two paths are exactly opposite, and the light destructively interferes. The power, however does not peak at a consistent maximum, since the mach-zhender has been optimized for a particular wavelength." + "The grating couplers are the input and output for light in the circuit. The Y-branch can split and recombine light, and because the waveguides which carry light across the circuit have a different length relative to each other, this produces interference when the light is recombined at the second Y-branch. We can now begin defining our circuit in Simphony using the components we have identified." ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ - "## Analysis of a balanced MZI" + "## Models" ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ - "You can think of a balanced interferometer as having equal path lengths when the light separates. In ideal conditions, this means that amount of power at the output is the same as the input. " + "We'll use models from the [SiEPIC Ebeam PDK](https://github.com/SiEPIC/SiEPIC_EBeam_PDK) library (already included in simphony)." ] }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "from simphony.libraries import siepic" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As explained in the Introduction, models in SAX (and therefore in Simphony) are simply \"callables\" (functions) that return a dictionary of scattering parameters. The keys of that resulting dictionary are the port-to-port relationships. In Simphony, we follow the convention ``(output, input)`` for the keys of the dictionary, which is the same as the S-parameter matrix formulation (where $S_{ij}$ is the scattering parameter representing the response at port $j$ given a stimuli at port $i$).\n", + "\n", + "Models in SAX must have default parameters in their function signatures; that is, no positional arguments are allowed." + ] + }, + { + "cell_type": "code", + "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "You can install `pip install gdsfactory[full]` for better visualization\n" + "\u001b[0;31mSignature:\u001b[0m\n", + "\u001b[0msiepic\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgrating_coupler\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mwl\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mUnion\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mfloat\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mjax\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mArray\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1.55\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mpol\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mLiteral\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'te'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'tm'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'te'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mthickness\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mfloat\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m220.0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mdwidth\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mfloat\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mDict\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mTuple\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mjaxtyping\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mComplex\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mArray\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'...'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mDocstring:\u001b[0m\n", + "SiEPIC EBeam PDK grating coupler optimized for TE polarizations at\n", + "1550nm.\n", + "\n", + "The grating coupler efficiently couples light from a fiber array positioned\n", + "above the chip into the circuit. For the TE mode, the angle is -25 degrees\n", + "[needs citation].\n", + "\n", + ".. image:: /_static/images/ebeam_gc_te1550.png\n", + " :alt: ebeam_bdc_te1550.png\n", + "\n", + "Parameters\n", + "----------\n", + "wl : float or Array\n", + " The wavelengths to evaluate at in microns.\n", + "pol : {\"te\", \"tm\"}\n", + " Polarization of the input/output modes.\n", + "thickness : {210.0, 220.0, 230.0}\n", + " Thickness of the grating coupler silicon in nm. Useful for simulating\n", + " manufacturing variability.\n", + "dwidth : {-20.0, 0.0, 20.0}\n", + " Change in width from nominal of the gratings. Representative of\n", + " manufacturing variability. Must be one of -20, 0, or 20.\n", + "\n", + "Raises\n", + "------\n", + "ValueError\n", + " If `pol` is not 'te' or 'tm'.\n", + "ValueError\n", + " If `thickness` is not one of 210, 220, or 230.\n", + "ValueError\n", + " If `dwidth` is not one of -20, 0, or 20.\n", + "\n", + "Notes\n", + "-----\n", + "See also the PDK documentation:\n", + "https://github.com/SiEPIC/SiEPIC_EBeam_PDK/blob/master/Documentation/SiEPIC_EBeam_PDK%20-%20Components%20with%20Models.docx\n", + "\u001b[0;31mFile:\u001b[0m ~/git/simphony/simphony/libraries/siepic/models.py\n", + "\u001b[0;31mType:\u001b[0m function" ] - }, + } + ], + "source": [ + "siepic.grating_coupler?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This means that they can always be called without arguments to inspect what their default return values are and to see what port names are provided." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ { "data": { - "image/png": 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" + "{('o0', 'o0'): Array([-0.0307378-0.00345908j], dtype=complex128),\n", + " ('o0', 'o1'): Array([0.75686856+0.02082852j], dtype=complex128),\n", + " ('o1', 'o0'): Array([0.74360676+0.09760613j], dtype=complex128),\n", + " ('o1', 'o1'): Array([0.0750638-0.02585451j], dtype=complex128)}" ] }, + "execution_count": 5, "metadata": {}, - "output_type": "display_data" - }, + "output_type": "execute_result" + } + ], + "source": [ + "siepic.grating_coupler()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "SAX also provides an explicit function to get the port names." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "my_component: uid 4dfe1431, ports [], references ['mzi_1'], 0 polygons\n" + "('port 1', 'port 2', 'port 3')\n", + "('o0', 'o1')\n", + "('o0', 'o1')\n" ] } ], "source": [ - "import gdsfactory as gf\n", - "\n", - "PDK = gf.get_generic_pdk()\n", - "PDK.activate()\n", - "\n", - "# The << is shorthand for c.add_ref()\n", - "c = gf.Component(\"my_component\")\n", - "mzi = c << gf.components.mzi(delta_length=0)\n", - "\n", - "c\n" + "print(sax.get_ports(siepic.y_branch))\n", + "print(sax.get_ports(siepic.waveguide))\n", + "print(sax.get_ports(siepic.grating_coupler))" ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ - "There are there three different relevant amplitudes:\n", - "1. The amplitude of the input light:\n", - "$\n", - " I_{input} = E_{input}^2\n", - "$\n", - "\n", - "2. The amplitudes of the beams after the split\n", - "$\n", - " E_{1} = \\frac{E_{input}}{\\sqrt{2}}, \\hspace{2mm} E_{2} = \\frac{E_{input}}{\\sqrt{2}}\n", - "$\n", - "\n", - "3. The amplitude of the recombined light\n", - "$\n", - " I_{output} = [\\frac{E_{1}+E_{2}}{\\sqrt{2}}]^2 = I_{input}\n", - "$\n", - "\n", - "```{warning} \n", - "Note that these are not generalized equations. Instead, these describe a balanced interferometer whose branches have not undergone a phase shift.\n", + "```{note}\n", + "Throughout all of Simphony and SAX, the ``\"wl\"`` argument is in microns, by convention. This is not strictly enforced, and\n", + "various model libraries may follow different convention, so it's always good to double check the documentation of the model you're using (or, if you're writing the model, create documentation for it)!\n", "```" ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ - "### Quick check\n", - "If intensity of the input wave to a balanced interferometer is 0.5 mW, what is the intensity at the output? Assume lossless waveguides.\n", - "\n", - "
\n", - " Answer\n", - " 0.5 mW \n", - "
" + "## Writing the netlist" ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ - "## Definitions" + "A SAX circuit contains a \n", + "netlist, which is a collection of component instances, their connections, and \n", + "exposed ports; and a list of models, which makes it easy to swap out different\n", + "models without rewriting your netlist to see how the circuit behavior\n", + "changes.\n", + "\n", + "The netlist is a dictionary with three fields:\n", + "\n", + "* ``\"instances\"``: A dictionary of instance names to human-readable component \n", + " model strings (like \"coupler\" or \"waveguide\"). You will define the \n", + " string-to-model mapping later.\n", + "* ``\"connections\"``: A dictionary of ports to ports in the form \n", + " ``\"instance_name,port_name\": \"instance_name,port_name\"`` (note there is no\n", + " whitespace delimiting the instance from its port, just a comma).\n", + "* ``\"ports\"``: Since a SAX circuit is basically a model itself, and could be\n", + " used in other circuits, it has exposed ports. This field is a dictionary \n", + " mapping port names of the composite object to the ports of its constituent\n", + " instances.\n", + "\n", + "We'll create " ] }, { - "attachments": {}, - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 7, "metadata": {}, + "outputs": [], "source": [ - "It would be helpful to mathematically model the light in our waveguides. We can start to build a model by considering the equation for a plane wave." + "mzi, info = sax.circuit(\n", + " netlist={\n", + " \"instances\": {\n", + " \"gc_in\": \"gc\",\n", + " \"splitter\": \"ybranch\",\n", + " \"long_wg\": \"waveguide\",\n", + " \"short_wg\": \"waveguide\",\n", + " \"combiner\": \"ybranch\",\n", + " \"gc_out\": \"gc\",\n", + " },\n", + " \"connections\": {\n", + " \"gc_in,o0\": \"splitter,port 1\",\n", + " \"splitter,port 2\": \"long_wg,o0\",\n", + " \"splitter,port 3\": \"short_wg,o0\",\n", + " \"long_wg,o1\": \"combiner,port 2\",\n", + " \"short_wg,o1\": \"combiner,port 3\",\n", + " \"combiner,port 1\": \"gc_out,o0\",\n", + " },\n", + " \"ports\": {\n", + " \"in\": \"gc_in,o1\",\n", + " \"out\": \"gc_out,o1\",\n", + " },\n", + " },\n", + " models={\n", + " \"ybranch\": siepic.y_branch,\n", + " \"waveguide\": siepic.waveguide,\n", + " \"gc\": siepic.grating_coupler,\n", + " }\n", + ")" ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ - "$$ \n", - "E = E_0 e^{i(\\omega t - \\beta z)}\n", - "$$\n", - "\n", - "Propagation Constant of Light:\n", - "$\n", - " \\beta = \\frac{2 \\pi n}{\\lambda} \n", - "$" + "## Simulation (using callables)" ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ - "If the equations above describe the propogation of the wave before the light gets split by the MZI, we can represent the two resulting beams as follows." + "```{eval-rst}\n", + ":py:func:`sax.circuit` returns a tuple. The first element is another callable function. All\n", + "parameters you call it with will be passed on to the models contained within the\n", + "circuit, so long as they are named the same. In this way, a circuit itself can \n", + "act as a model within another circuit.\n", + "```\n", + "\n", + "```{warning}\n", + "It is important that your all models have **common names** for arguments. For example,\n", + "all models that take a ``length`` parameter should all use the name ``length`` for that argument. Models\n", + "that are wavelength dependent should all take the same ``wl`` keyword parameter (by \n", + "convention). If you have your own model library, you can follow whatever \n", + "convention you want, as long as it's consistent.\n", + "```\n", + "\n", + "The second element of the returned tuple is a information object that contains details\n", + "about the circuit.\n", + "\n", + "We can simulate the circuit by simply calling it with the appropriate arguments--in this case, the wavelengths we want to\n", + "simulate at.\n", + "\n", + "The circuit itself contains parameterized models. We can pass arguments targeting\n", + "those models by passing a dictionary of keyword arguments and corresponding values\n", + "when invoking the circuit function. The names you gave the instances\n", + "in the netlist become the keyword arguments of the function. You can pass a \n", + "dictionary which will be used to instantiate those components at simulation time.\n", + "\n", + "If you're unsure of the format of the settings dictionary you need to pass to the circuit, you can use SAX's get_settings function." ] }, { - "attachments": {}, - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 8, "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'gc_in': {'wl': Array(1.55, dtype=float64),\n", + " 'pol': 'te',\n", + " 'thickness': Array(220., dtype=float64),\n", + " 'dwidth': Array(0., dtype=float64)},\n", + " 'splitter': {'wl': Array(1.55, dtype=float64),\n", + " 'pol': 'te',\n", + " 'thickness': Array(220., dtype=float64),\n", + " 'width': Array(500., dtype=float64)},\n", + " 'long_wg': {'wl': Array(1.55, dtype=float64),\n", + " 'pol': 'te',\n", + " 'length': Array(0., dtype=float64),\n", + " 'width': Array(500., dtype=float64),\n", + " 'height': Array(220., dtype=float64),\n", + " 'loss': Array(0., dtype=float64)},\n", + " 'short_wg': {'wl': Array(1.55, dtype=float64),\n", + " 'pol': 'te',\n", + " 'length': Array(0., dtype=float64),\n", + " 'width': Array(500., dtype=float64),\n", + " 'height': Array(220., dtype=float64),\n", + " 'loss': Array(0., dtype=float64)},\n", + " 'combiner': {'wl': Array(1.55, dtype=float64),\n", + " 'pol': 'te',\n", + " 'thickness': Array(220., dtype=float64),\n", + " 'width': Array(500., dtype=float64)},\n", + " 'gc_out': {'wl': Array(1.55, dtype=float64),\n", + " 'pol': 'te',\n", + " 'thickness': Array(220., dtype=float64),\n", + " 'dwidth': Array(0., dtype=float64)}}" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "$$\n", - "E_{o1} = \\frac{E_{i}}{\\sqrt{2}}e^{-i\\beta_{1}L_{1}-\\frac{\\alpha_{1}}{2}L_{1}}\n", - "$$" + "sax.get_settings(mzi)" ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ - "$$\n", - "E_{o2} = \\frac{E_{i}}{\\sqrt{2}}e^{-i\\beta_{2}L_{2}-\\frac{\\alpha_{2}}{2}L_{2}}\n", - "$$" + "This is slightly overkill in our case. We don't want to update ``wl`` for every single object. We only want to change the length parameters for the long and short waveguides, and let everything else stay with the default or assume a global value.\n", + "\n", + "We can set the ``\"long_wg\"`` and ``\"short_wg\"`` waveguides to different lengths alone. The waveguide model within the circuit takes a \"length\" argument, so that's what we'll use in our dictionary of parameters that we pass using keyword arguments. These keyword arguments correspond to the *instance name* in the netlist. By setting ``wl`` at the toplevel, it will trickle down to all models that require a ``wl`` parameter." ] }, { - "attachments": {}, - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 9, "metadata": {}, + "outputs": [], "source": [ - "*** As the light travels through the waveguide we can imagine that it will experience some degree of loss. The 'α' term that appeared in the exponents is the loss coefficient. For convenience, the following examples will assume that α = 0, or that there is zero loss in our waveguides." + "wl = jnp.linspace(1.5, 1.6, 1000)\n", + "S = mzi(wl=wl, long_wg={\"length\": 150.0}, short_wg={\"length\": 50.0})" ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ - "And finally the intensity of the the recombined light at the end of the MZI can be described like so:" + "``S`` is now our evaluated s-parameter dictionary" ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ - "$$\n", - "I_{output} = \\frac{I_{input}}{4} \\lvert E_{o1} + E_{o2} \\lvert ^2 \n", - "$$" + "We're interested in the power transmitted from the input to the output, which is the magnitude squared of the s-parameter. We'll square the magnitude of the ``\"out,in\"`` element of the resulting dictionary. Recall, too, that we renamed these \"external\" (unconnected) ports in the netlist when we created the circuit. It's really easy to give the ports on your composite circuits meaningful names, and it makes your code much more readable.\n", + "\n", + "Below, we plot both in linear and in log scale using [``matplotlib``](https://matplotlib.org/)." ] }, { - "attachments": {}, - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 10, "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "## Analysis of imblanced MZI's" + "mag = jnp.abs(S[\"out\", \"in\"])**2\n", + "\n", + "fig, axs = plt.subplots(2, 1, sharex=True)\n", + "axs[0].plot(wl, mag)\n", + "axs[0].set_ylabel(\"Transmission\")\n", + "axs[1].plot(wl, 10*jnp.log10(mag))\n", + "axs[1].set_ylabel(\"Transmission (dB)\")\n", + "axs[1].set_xlabel(\"Wavelength (um)\")\n", + "plt.suptitle(\"MZI Response\")\n", + "plt.show()" ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ - "The equation below is the simplified version of the above expressions assuming no difference in the propogation constants of the two different waveguides and no loss." + "As you can see, and as you might have predicted, by varying the wavelength we can see different levels of power being output from the device. Notice how the power periodically dips to 0 (on the linear plot). This occurs when the phases of the two paths are exactly opposite, and the light destructively interferes. The power, however does not peak at a consistent maximum--an effect of using grating couplers, which are optimized for better coupling at some center wavelength (in this case, 1.55 microns)." ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ - "$$\n", - "I_{output} = \\frac{I_{input}}{2}(1 + cos(\\beta \\Delta L))\n", - "$$" + "## Analysis of a balanced MZI\n", + "\n", + "You can think of a balanced interferometer as having equal path lengths when the light separates. In ideal conditions, this means that amount of power at the output is the same as the input. " ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "You can install `pip install gdsfactory[full]` for better visualization\n" + "\u001b[32m2024-01-09 23:00:01.222\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mgdsfactory.technology.layer_views\u001b[0m:\u001b[36m__init__\u001b[0m:\u001b[36m785\u001b[0m - \u001b[1mImporting LayerViews from YAML file: '/home/sequoia/git/Photonics-Bootcamp/env/lib/python3.11/site-packages/gdsfactory/generic_tech/layer_views.yaml'.\u001b[0m\n", + "\u001b[32m2024-01-09 23:00:01.227\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mgdsfactory.pdk\u001b[0m:\u001b[36mactivate\u001b[0m:\u001b[36m258\u001b[0m - \u001b[1m'generic' PDK is now active\u001b[0m\n" ] }, { "data": { - "image/png": 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hhagURKMSWOx26QXgFYKzr6xMFtqzWKTW0Wi+W0RUNBhWiEpBNCo9KoDsUBlWss/lkjCYSEhgYVghyhqGFaJSEIvJR713RT9zhbLH4ZC66qGQYYUoaxhWiEpBPC4fk0kJK5xca4yyMqmxzcY5K0RZxLBCVAqiUTmlVtPYq2Ik/aKGVmu6N4uIJoxhhagU6Ot/aBrnqxhJX3DPZpMhN31IiIgmhGGFqNjpC8HpYYVDQMax2eSj1Sp1Z1ghygqGFaJip2lys1rTR/1kDL22FovUnGGFKCsYVoiKnabJUb7FIl9b+bI3jF5bi0VqzlVsibKC71pExY5hJXf03is9rCiV7xYRFQW+axEVO32nqYcV/SNlX2Zt2bNClDUMK0TFbvDRPcOKcfReFYA9K0RZxLBCVOzYs5I7mWGFiLKGYYWolPBIPzf0OrPeRFnBsEJUSnjkbyzWlsgQDCtERERkagwrRKUgmQQiEVkCXr+oIRkjmQR6e6XOrDVRVtjz3QAiMlgyCQSDsuO0WIAjR4CKCl7Q0AisNZEhGFaIip3DAVRWpheD4/ofxtFrrS+7r1/qgIgmhMNARKXAYpGjex7hG89ikdDCC0YSZQ3DChEREZkawwoRERGZGsMKERERmRrDChEREZkawwoRERGZmqFh5Y033sAtt9yC+vp6WCwW/PznPx9wv1IKzc3NqK+vR3l5ORYtWoR9+/YZ2SSi0qOULAbX1yc3Mg5rTWQIQ8NKb28v/u7v/g5PPfXUsPd/5zvfwRNPPIGnnnoKu3btQm1tLW644QaEQiEjm0VUWmIxoKcHOHkS6OiQdT+s7FQ1BGtNZAhDF4W7+eabcfPNNw97n1IKGzduxEMPPYTbbrsNAPD888+jpqYGL774Ir74xS8a2TSi0uFwyCqqDocc7Tc1cb0Vo2TWOhIBZsxgrYmyIG8r2B4+fBjt7e1YsmRJapvL5cJ1112H7du3jxhWotEootFo6utgMAgA0DQNGleKzCq9pqyt8QytczIpR/dOpxz522wlu6qq4c9nvSfF6QSiUdaa7x2GK+Q6j6fNeQsr7e3tAICampoB22tqanD06NERv2/Dhg1Yu3btkO2tra3w+XzZbWSJi0QiCIVCOHbsGNxud76bU9SUUuju7obFYoHFYsnuD49G0xcx7O8H2tvl+jUlyNA6AxIGM2t98iRQosPahteaABR2nccz5SPv1wYaXFyl1KgFX7NmDVavXp36OhgMIhAIIBAIwO/3G9bOUhQOh3HgwAE0NDTA6/XmuzlFTdM0KKUQCARgzfYch95emUfhdstS8HV1MlRRggytMyBB5cwZoLxcvq6pkWsFlSDDa00ACrvOwXEcNOUtrNTW1gKQHpa6urrU9o6OjiG9LZlcLhdcLteQ7VarteD+UWan15S1zQ3Daq2Hf4tFblZrSU/6NPQ5bbOla6vXmbXm+4fBCrXO42lv3v6ypqYm1NbWYsuWLaltsVgMW7duxZVXXpmvZhEVNz2wUG4ole8WEBUFQ3tWwuEw3nvvvdTXhw8fxp49e1BVVYXGxkasWrUK69evx+zZszF79mysX78ebrcbd955p5HNIiotmjZwp8mwYhyrVeqr15thhSgrDA0r77zzDhYvXpz6Wp9rsnz5cjz33HO4//770dfXh7vvvhvd3d1YsGABNm/ezImyRNmkVHqnyZ4VYw2ubwGeoUFkRoaGlUWLFkGNcmRhsVjQ3NyM5uZmI5tBVNr0nhWl0nNWyBiDe1YYVoiygu9aRMVO09I7UIYVY+lhRQ8pDCtEWcF3LaJil0zKR33BMpstv+0pZvrZP3pI0WtPRBPCsEJU7BhWcsvhSPdixWL5bg1RUWBYISp2+g5T0wC7ncNARnO50pc4YFghygq+axEVu/5+6U1JJoGysny3pvi5XNKzYrPJpQ54+jLRhDGsEBUzpQZeUG+Y1Z8py/RhID0gxuP5bhFRwWNYISpmsZhcVM9ulx0ow4rxnE6Zr2KzSe05FEQ0YQwrRMUsGpUje5tNdqAMK8ZzOtPhMJmUYTgimhCGFaJi1t+f3mna7QwrueBySa0TCQmIDCtEE8awQlTMentlhxmPy06UYcV4Nhvg8UjN7XYgGMx3i4gKHsMKUbHSNCAUkgmf8Tjg9/O6QLni80lvlsMhPSvRaL5bRFTQGFaIilVvr+wonU752uPJb3tKidudXoAvGgXC4Xy3iKigMawQFatgUI7u9SN8rzffLSodHo+saROLSWjp6cl3i4gKGsMKUTHSNKC7W0JKNCpBhQvC5Y7NBkyaJGHF5ZKwwlOYic4bwwpRMQoGZRiorEyCS1VVvltUeiZNkjlCdrsMx505k+8WERUshhWiYqMUcPq0fNTPAqqoyHerSo/PJ8NB/f0SWE6dSl+NmYjGhWGFqNiEwzIE5HbLjnLy5PQkW8odqxWYMkUCY3l5+v9CROPGsEJUTJQCTp6USbVKyZyVyZPz3arSVVkpQSUalSGh9nb53xDRuDCsEBWTM2eAzk4ZfohEJKjwlOX8cTqBmhqZXOt2y7o3p0/nu1VEBYdhhahYxOPA8eMDV6ytrc13q6i6Ws7G6uuTnq62NvmciMaMYYWoGCglO8FQSI7go1EJKuXl+W4ZORxAXZ1MrnU6ZR7RsWOcbEs0DgwrRMWgs1PmQ3g8MpGzogKYOjXfrSJdVZUMyYXD0sty+jRw4kS+W0VUMBhWiApdKAS0tMhCZImEHL0HAnK6LJmD1Qo0NEivV1+f9Hi1tUnIJKJzYlghKmSRCHD4sMxRcTrl47RpssYHmUtZGdDYKHOKlJKPR49yKX6iMWBYISpUkQhw6JB8dLtlxdraWg7/mFllpYTJaFTCZSIhYZOBhWhUDCtEhSgUAg4eTM+BCIdlAbJp0+SIncyrtlYm3OqXQ4jFJHRywTiiETGsEBUSpWSew3vvSY+KxyPXAZo8GZg+nfNUCoHVKnOKamvTgSWRkPDZ3s6zhIiGwXc2okKRSMikzPZ22eHpS7hPnSpBxeHIdwtprKxWmb9itcpZQfoFJ48ckRA6bZqsk0NEABhWiMxPKek9OX5c5ja43bJkeyQiR+c886cw2Wzp/93x4xJcPB65XEI4LIGlqorDekRgWCEyt95e2Xl1dsqRt88n22w2YMYM6VWxcjS3YFmtQH299KK0tsr/1ueT05sPHJCwUlsr2xhaqIQxrBCZjVIygbazU27xuBxxa5ps9/vliNzvz3dLKRssFlmS3+2WwNLdLUN6ZWXyeU+PnEVUXS3/c4ZTGqPjPT348d69+OW77+Kvp06hPRxGVXk5rpo+Hfdfey0WNDbmu4ljxrCSQy/893/jzSNHsPv4cextb0csmcSzH/84Vsybl++mUb4pJWeFnDwpO6jeXpmj4nbLKa6RiOzAAgG5MB7npxQftxuYNQs4dUrmsYTDMi/JapUVb/ULVE6eLD0tbjeDC43qyR078L+3bsUFVVW4YfZsTPV4cKCzEz/fvx8/378f/98dd+ATl12W72aOCcNKDv3z5s04euYMqj0e1Pl8OHrmTL6bdG7JpFzLxKg5EYlE+qJ7FsvYbsUgmZS1Nvr65KZfLfn06fRRdTIp99lsclpybS2voFzsbDb5P0+aJMH19Gl5DpSVyfOiv18m4drtsq2iQkJLebm8hopl7pJSY78BUjcjWa1y0FBgPtjQgDf+1//CNU1NA7a/efgwrn/mGXz55z/Hx+bMgasAnjfmb2ER+fd//EfMnjwZ0ysr8cjrr2PNb36T7yaNLhaTHej//I9xF8Tr7ZVbVVX6jTYzlAz30WpNfwTkjSpzu8Uy/LZs3jRNfof+ZqnT30A1LX1LJiWUJRISUPr75WM8Ltv09tpssmZKNCpH1S6X9KLoV+0tlqBG51ZWJmd4TZmS7lUJheQ54vHIcyEWk7PDlJLXjt0uO9Tycvl+u10eb7fL6yDztTC4R0Z/TuvGExZGuiWTEsL136u/HvTXR+brZPC2zDbonw+3TT+Y8noN/XfA6QQuvthUgeX53buxaedO/OXkSShNw6V1dfjyggVYntFTf9vcucN+7zVNTVg8cyY2HziAve3tmN/QkKtmnzdThJWnn34ajz76KE6cOIFLLrkEGzduxDXXXJPvZo1Z5pMGAObW1KSeNEopROJxAMDCs+ODavAOzqz0Ha3VatxplJFIumdFDyujvUHpbRrpzWuk7xvLjl5fAj3z8cMFp3hcwoR+pkbm/zPzd+tvvpk/OzOYlJXJ1/G4BJRYTN549a7+igpeNbnUud1yinNNjcxd6eqS5148nj7adzjkeZRISC9MODw0RGcG98xtmR97etK9esDYXmMjva707+nvl9e4Ho5Ge12Ndl9m2zM/9vTI60YPZUZIJOR3mGj9m6+98go2/uEPmOb343Pz5iEcCmFLaytW/OQneOf4cTxy002px7odDliG+T85zv5P7AUylJj3sPKjH/0Iq1atwtNPP42rrroK3/ve93DzzTdj//79aCyAyT+ZT5rPz58PC4Cf7tuHFT/5Cf504gT+nyVL8Ogbbwz4nq9fe63xDcvGkdGZM/ICtduNmyOhd98a+TvG61xHdHrPyHDtHe6NdnAvi97TEo3K3+9wSPBxu4GmJvai0FAul5z5NWWKPP/CYelpCYXk62RSHpcZhvXelMyd0eDhk0yRSLp3T++ZHC1QnItSEiYqKox7Pus/18j3j0RCanzmTPoAYyK3CXrz8GFs/MMf8L6pU7Hjy1+Gz+lES0sLHp86FR/atAlP7diB3lgMjZMmAZD9jWdQj1DLmTP47cGDqPX5cGlt7YTblAt5DytPPPEEPv/5z+MLX/gCAGDjxo34zW9+g02bNmHDhg15bt3oBj9pKs4ekay94QZ86Omn8X/+8AfcfNFF8mB9B6WHgL4+2R4OSzcvkD4SH9yToH80oht1tG2hkOxQzeJ8/oaJPH6kN5ZEQnYAvb2jv/lkdrnrt/LydDe9yyW38nJ5XEtLuoufaDgWS/o5NGWKvNb7+9MBur9f3lvicXnPiceH9vLpBj/HLZZ0EBrcS5HZkzLWHhGl5Pf39Y3cMzLazzTL66C3V4bhlEoHorG2fbhtmcPYme8Pg7eNMIz93B/+AABovuIKVITD0JQCentR0deHNR/8IP7pl7/EnmPH0OB2wzrMsFU8mcSy//t/EU0k8J2bboKNPSvnFovFsHv3bjz44IMDti9ZsgTbt2/PU6vG7rk//hEA0Hz99amgAgAVZWV4+Prr8amXXsIP9+zB8WAQ+9ragGQSl1dUyOS5U6fkwSdPSleu/uLWn9AjfZ7tbtTRfoYeqIymH4GNpRv3fN8khnvx659nHkUO98Yx+KbvIAaPkw/3ZmS1po909bkDwzFRFzMVEKtVeuTc7oHbM+dK6Z8PPhgaHF70XppJk+TnDtfbOtyB0UgHTclk+jUz+HeO5wBitNASj+cu1EyaJGHlfA8C9VsiMfRxo/0MvX4AYLHgv48dAwAsSiblshtKyXt1Tw+uPXtw+ZeODrQHg3hfff2AnnxN0/C5n/wEbxw+jLuuuALLPvCBbFXHcHkNK6dPn0YymURNTc2A7TU1NWhvbx/2e6LRKKIZR/vBYBAAEA6HYc1xQtzd2goAuKKmBuFweMB9V5z9m/77+HF09PamdkYhTUPY4UD07I65325H2Ij5IIOf9Och3NeHvmgU4b4+47pY9bFgfTgks9t6pCOPzPszb4N7MfQgMrgrPBvGM6FPH/qJxUZ8iKZp6O3tzcvzuJSUbJ3H8hrIOODKBk3T0NvSgnB9vVyETg8wgwPOWHuLM3uZM7f19qZP7TdCX5/0WmXrd4xnKG0YPYkErADKvV6ELRZoSqG3vx9hlwtumw0WABoApWnYf/IkTnZ1YarXC6UUvvLqq3jhT3/CHXPn4rEbbhiy38q18fz+vA8DARgy+UcpNeyEIADYsGED1q5dO2T7m2++CffgowuDnezpgRXA3p07h/0brAA6zpzBDI8H7/b3A0ohGA7jjd27cSgUAgD89dAhvNbRMbZfONKTe3BX7kjbRvtZwzy+v6cH7548Ccv+/SgzqrZ9ffImoF/v5lzO9bec6+8f3B2ry5wAeK4eJ/3oKPONa7jfkfl7MntphqGUQjAYxMGDB0d87tPElUydM3tCBg8LZx7AZH6uD2/qr4Vz9RAAo55BpJRCMBbDQacTlszHDP69wx1QjfUgSz8QOHHCuNOXIxF5n9LPxhqt7ef6W8b694+2PR6HBuDnb72FSTYbFIBgPI6DDgdOxuNQZ783mkjAY7Vi91tvwWm14smDB/G7jg5cW12NT/p82Pr666P+2bkQiUTG/Ni8hpXq6mrYbLYhvSgdHR1Delt0a9aswerVq1NfB4NBBAIBXHPNNfDneEXPmoMH0d7fj0sXLMCUQetfdITD0HbswNRJk7D4wgtxXSIB69mzWK69/HLs//OfgbfewsWXXorFF100cjfp4O5X/UwYfRsw/BFJ5s/INNYuTADhYBAqGsU1c+bAa1Rtu7rkdq4L8Z1rfsm5tp3PzxjU/Zrarh9lVVYOHb7L/N7hxqUdjvQ8Ff3Mi7IyaBYLWltbEQgESuuIP8c0TSuuOutDkvrZZHovgN5jObiHYrjns/55d7d8XVGR3vGPNAwzhqFkDUBrJIKAxwPr+Q5fnytQdnfLHJvaWuN6Vjo75fdUVg5cXmG0No90MDj4fWG0IerBBzlnb1e98QYO/e1vUBddhMWzZkFTCq2nTyMwdSpeOnAAaGvDJdXVWDprFuBw4OoFC3D/5s34XUcH/nHOHDzzsY+ZZp6KPjIyFnkNK06nE/PmzcOWLVvwD//wD6ntW7Zswcc+9rFhv8flcsE1zLCJ1+uF1+hz7QeZFwjgTydPYtfJk0NWAfzloUMAgPfX18PpdA44P987bRpcR48CAMqqq+GdMSP7jRscds7n1tmJ8hMn4C0vh9fInhWXS8bczXQ2kP5xuBDT0yOhsbp64PcN92Y1+AhXvwBhOJwepnI4oHk88GgavFYrrJxkaxhN0+DxeOD1egszrCgl4aS3V470g8H0ej1A+jnlcqWHQId7PuqfZ+rtlednVVX6/Wq0nfI5aErBY7fDW1GRDivZpp8JZeT7h8UiBxezZg08GwgYGib0bZn3Dd42QV+wWvEff/sb/vfevbj16qvhdTrhsVigTZmC//PTnwIAPtDYCKfHA6UU7v/tb/HCn/+M2y+9FC9+8pOwG72A3jho45irl/dhoNWrV2PZsmWYP38+Fi5ciO9///toaWnBl770pXw37ZyWf+AD+H/feQdrf/c73HThhfCfHfMN9vdj7e9+BwD43Pz5QxbccedipzzKkMOY6Wus5IK+ONpIAUH/ONp9Iz1mJJm9J8P1ooz0Rq2vbxGLDdwJZP7ckbrc9Qm3mevKxONy9BaJpNdZqaqSCX1ZnkdABSoWk5Dc2ZleZ8VikeehyyXPk0Ri4AKEmQZPEs/crtOfa/qk3NFeb2OhlByMaNrEzgYa7b7xtOd86asFm+T1eG1TE766cCGe3LEDczduxG2XXIJgMIjftraitacHX16wAI9+5CMAgHWvvYYX9uyB1+nEhdXV+PZrrw35ebfOmYP319fn+s8Yt7yHlU9+8pPo7OzEt771LZw4cQJz587FL3/5S0yfPj3fTTunwU+af5w7F0opvLxvH1p7enDPlVfiupkzU4//9127sO3IEQDA3rNDX/++axdeP9sLc+ucObj1kkty/neck74kvhH0N7L+/nRgAUZ+sxqt+3SkUwBHOvo5nyMi/Wcmk0PHyAcP2w23tkrmaaZ9fen1MfQA43LJEa6+pkNlpZyimuNeQzKJSCS9gm1/f3oxwbKy9HBP5no9Lpc8Z/QwrK9iO9KwQiZNG9oDM5FbMilt14dLM4elMoe0M7cNF5KGmxejf67/TYPDWTYZ+bPP03c/+lFcXl+PTTt34ge7dkEphbk1NVj74Q/jn+bPTz2uTT8BJRbDumGCCgDMqKwsiLBiUQWznOrwgsEgKioq0NPTk/M5K7pn33kHm3buxL6zK9hecnYF28wnDQCs+PGP8fzZ052H8/D116P5wx82tK3jEe7qwms/+xkWX3ghvEatpKr3asyalT6Fe7jJriMdGRYqfVXMvj6gtxdaTw9aurrQ6HDAOvjaQHa79LTU1Q09RZXGRdM0tLS0oLGx0dzDQP39QEeHLHEQiw28NlA8Ls8Jl0vml3g86QBjomu8TKjWmfPxgKFz9PRt0ahcK0kP/UYx4XL7uoJ5Tg9jPPtv8zyzC9g/zZ8/JJgM57nbb8dzt9+egxZlidMpy76/733GHtkX6EXCJkQ/6nW7pcbJJHDwoNT5zBnp6tc0ud9ikR3XmTMykbCmxlQ7JcoiTUtfdbmvT+ZKeDzyeSwmn9fXA35/cV91eaxzPNxuqYnR6xSV4nuUyfAdj0aX2e1MxtHnH9TWyk1f2birS3phPB4JNC0tEloCAdlhUfGIRIDWVvmfO50SXHt75TVYVSUTuisqijegnC+GiJLAsEJkNlarBBG/X3pR2tvTy337fBJkDhwApk2T+4thWKzUdXZKUOnrk/+xvv5QVZWEV5+P/2cqaQwrRGbm8QAzZ8pQ0fHjckaI3sty5IjMYWho4LBQodI0GfI5flzCiNcrpyTrwz1VVexJIQLDCpH5WSxy2qTXC7S1pVf7dbtlR5dInHtRPTIfTZPelBMnZJhVKRn2mTpVAqgRl+EgKlAMK0SFwm6XuSput8xd6euTAHPqlOz4Zs5kD0uh0DTg6FEJnh6PnNVisUjorKlhbwrRIHxnIyokFotMtHS5gMOH5Ujc55M5D1Yr0NRk3DVSKDuUkh6VkyfTZ/o4HMCMGTLsQ0RDML4TFSKfT9am8Xhkwq3ew3LsWG5W9aTz194uQz9ut8w5cjqBCy5gUCEaBcMKUaFyu2Un53bLmSMej+wIx3oVb8q97m4JlC6XrJtit8vwXUVFvltGZGoMK0SFzO2W4QOHI72y6fHjckYJmUt/vwz/ZF6Lavp0BhWiMWBYISp0fj/Q2CinMzsccsR+7Jgpr2lSspSSEBkOp4d/6uvllHQiOieGFaJiMHmynEXS25tesp/DQebR1SVzinw+6fXSr/VERGPCsEJUDCwWOVL3+WT+issl81f6+vLdMorHZX0cmy19UcJAgKcnE40DXy1ExcLhkCX4lZLPo1EJLJRfp0/L8E95uYSVujr5nIjGjGGFqJhMmiRDQr29Mjeis1N6Wig/4nFZT8XplP+D3w9MmZLvVhEVHIYVomJiscjcFZtNPo/H5cie8qOrS4biXC5Ztba2lov2EZ0HhhWiYuP1ApWVciRfVia9K7FYvltVejRNJtXa7emrKU+alO9WERUkhhWiYqMvyW+xyNyV/n65WjPlVigkw3Hl5XIa+ZQp7FUhOk8MK0TFyO9Pr+dhtcpwBOVWT49Mdk4kZBiIi78RnTeGFaJiZLXKWh7xuAwFhcMSXCg3kklZWt/plLOyKioksBDReWFYISpWfr8MO9hsElp6e/PdotLR2yshxemUuSucq0I0IQwrRMXK7U5fMA+Q3hXKjUhEelc0TQKLx5PvFhEVNIYVomJls0nvSiwmE22DQZlDQcYLh9Mr1paXy1AcEZ03hhWiYqYf0esr2kaj+W1PKdA0CSsOh0yu9fvz3SKigsewQlTMysrkFGabTXacDCvG6++XWtvt8jV7VYgmjGGFqJi5XHKEn0zKEBDDivFiMQkrFouclcWwQjRhDCtExczplCN8fefJlWyNF4tJMEwmpfZOZ75bRFTwGFaIipnFIr0ryaQc5XOtFePpgTCZlOE3hyO/7SEqAgwrRMWurCy942RYMV4sJsEwmZReFYsl3y0iKngMK0TFTh+GsFplOEjT8tueYheNSjDUNK5aS5QlDCtExU6/eJ7VKjvQZDK/7Sl2+vwgfUE4IpowhhWiYqeHFX0HyrBiHL2+Vmv6lHEimjCGFaJiZ7XK2Sn6Rw4DGUfTpMb6PBUr32KJsoGvJKJipx/lWywMK0bTw4oeUhhWiLKCrySiYqcHFUB2pLw+kHEG15dhhSgrDH0lrVu3DldeeSXcbjcmjXCJ9JaWFtxyyy3weDyorq7GPffcgxgXriLKnsE7TIYV4wweBiKirLAb+cNjsRhuv/12LFy4EM8888yQ+5PJJJYuXYopU6Zg27Zt6OzsxPLly6GUwpNPPmlk04hKC3tWci9zOIiIJsTQsLJ27VoAwHPPPTfs/Zs3b8b+/fvR2tqK+vp6AMDjjz+OFStWYN26dfDzaqVEE5c5DETG0sMg602UVYaGlXPZsWMH5s6dmwoqAHDjjTciGo1i9+7dWLx48ZDviUajiGZcjC0YDAIANE2DxomDWaXXlLU1nqF11ocm9FsyWbKTbA1/Pg+utaax1iX69+dKIdd5PG3Oa1hpb29HTU3NgG2VlZVwOp1ob28f9ns2bNiQ6rHJ1NraCp/PZ0g7S1UkEkEoFMKxY8fgdrvz3ZyippRCd3c3LBYLLNk+Ko9GgUhEFivr7wfa24GzIb/UGFpnQJbaz6z1yZNAKJT931MADK81ASjsOofG8doYd1hpbm4eNixk2rVrF+bPnz+mnzdccZVSIxZ9zZo1WL16derrYDCIQCCAQCDAYaMsC4fDOHDgABoaGuD1evPdnKKmaRqUUggEArBme55DJAL09MjS7xYLUFsLjDDhvdgZWmdAan3mTHqZ/ZoaoLIy+7+nABheawJQ2HUOjuOgadxhZeXKlbjjjjtGfcyMGTPG9LNqa2uxc+fOAdu6u7sRj8eH9LjoXC4XXMNcb8NqtRbcP8rs9JqytrlhWK1tNrn19gJ2uwxLlPD/09Dn9OBal/gkW75/5Eah1nk87R13WKmurkZ1dfV4v21YCxcuxLp163DixAnU1dUBkEm3LpcL8+bNy8rvICp5mW8IySRw/Djg9/O6NUZgrYkMYeiclZaWFnR1daGlpQXJZBJ79uwBAMyaNQterxdLlizBnDlzsGzZMjz66KPo6urCfffdh7vuuotDOkTZok/2LC+Xr+Pxkp30aTjWmsgQhoaVb37zm3j++edTX19++eUAgNdeew2LFi2CzWbDq6++irvvvhtXXXUVysvLceedd+Kxxx4zsllEpcl+9uXOCxkaj7UmyipDw8pzzz034horusbGRrzyyitGNoOIiIgKWGHNxiEiIqKSw7BCREREpsawQkRERKbGsEJERESmxrBCREREpsawQkRERKbGsEJERESmxrBCREREpsawQkRERKbGsEJERESmxrBCREREpsawQkRERKbGsEJERESmxrBCREREpsawQkRERKbGsEJERESmxrBCREREpsawQkRERKbGsEJERESmxrBCREREpsawQkRERKbGsEJERESmxrBCREREpsawQkRERKbGsEJERESmxrBCREREpsawQkRERKbGsEJERESmxrBCREREpsawQkRERKbGsEJERESmxrBCREREpsawQkRERKbGsEJERESmZlhYOXLkCD7/+c+jqakJ5eXluOCCC/Dwww8jFosNeFxLSwtuueUWeDweVFdX45577hnyGCIiIipddqN+8F//+ldomobvfe97mDVrFv7yl7/grrvuQm9vLx577DEAQDKZxNKlSzFlyhRs27YNnZ2dWL58OZRSePLJJ41qGhERERUQw8LKTTfdhJtuuin19cyZM/Huu+9i06ZNqbCyefNm7N+/H62traivrwcAPP7441ixYgXWrVsHv99vVPOIiIioQBgWVobT09ODqqqq1Nc7duzA3LlzU0EFAG688UZEo1Hs3r0bixcvHvIzotEootFo6utgMAgA0DQNmqYZ2PrSo9eUtTWeoXXWNCCZBBIJ+Vop2VaC/1PDn896XRMJwGJhrfneYbhCrvN42pyzsHLw4EE8+eSTePzxx1Pb2tvbUVNTM+BxlZWVcDqdaG9vH/bnbNiwAWvXrh2yvbW1FT6fL7uNLnGRSAShUAjHjh2D2+3Od3OKmlIK3d3dsFgssFgs2f3hfX3AqVPpr8vLgePHAYcju7+nABhaZ0Bq3dGR/pq1Nq7WBKCw6xwKhcb82HGHlebm5mHDQqZdu3Zh/vz5qa/b2tpw00034fbbb8cXvvCFAY8drrhKqRGLvmbNGqxevTr1dTAYRCAQQCAQ4LBRloXDYRw4cAANDQ3wer35bk5R0zQNSikEAgFYrVme937mDHDoUPoI3+MBAgHA6czu7ykAhtYZYK0zGF5rAlDYddZHRsZi3GFl5cqVuOOOO0Z9zIwZM1Kft7W1YfHixVi4cCG+//3vD3hcbW0tdu7cOWBbd3c34vH4kB4XncvlgsvlGrLdarUW3D/K7PSasra5YVity8qAyZPlKD8UAi64QLaVKEOf03qtXS6gt5e15vtHThRqncfT3nGHlerqalRXV4/pscePH8fixYsxb948PPvss0MatnDhQqxbtw4nTpxAXV0dAJl063K5MG/evPE2jYhGYrPJzW4vySGJnLLZpMasNVHWGDZnpa2tDYsWLUJjYyMee+wxnMoYM6+trQUALFmyBHPmzMGyZcvw6KOPoqurC/fddx/uuusuDukQGUEpuZEx9NoW2NwBIrMzLKxs3rwZ7733Ht577z00NDQMuE+dfUHbbDa8+uqruPvuu3HVVVehvLwcd955Z+rUZiLKMu5Ec4v1JsoKw8LKihUrsGLFinM+rrGxEa+88opRzSAiiyV9Gi3AnhUjseeKyBCFNRuHiMZv8NE9d6bGyQwrekgkogljWCEqduxZyZ3M2losQIGdnUFkVnwlERU7q3VgWCnAlS4Lhqale1cYVoiyhq8komJntcqNYcV4yaR81MMKh4GIsoJhhajY6T0r+lG/vkOl7NODoFJSd5stv+0hKhIMK0TFzmKRxck0TXagsVi+W1S89CCoaVJ3hhWirGBYISoFTifDSi7E4/IxmZQVbBlWiLKCYYWoFLhcA8MKzwgyRjQqNda0krx4IZFRGFaISoF+jRq7HUgk5EbZpRTQ3y+9KclkSV/AkCjbGFaISoF+lG+zSVDhUFD26XW1n10YfJirwxPR+WFYISoFLld6/kQyKcMVlF3RqAQWm016WRhWiLKGYYWoFLhc6SEgfbiCsqu/P316uMPBsEKURQwrRKXAbgfKy+VsFbsdCIXy3aLiE4nIx3hcwgrnrBBlDcMKUanw+6VnxeGQHat+mi1NnKYBwaDUNh4HfD4utU+URXw1EZUKtzu9QFwsBoTD+W5R8ejrk5vTKcNAXm++W0RUVBhWiEqFxyPzKPR1VoLBfLeoeIRC6dPB7XaGFaIsY1ghKhUOB1BRIWetOJ1AdzfXW8kGpYCuLgkp/f3Sg1Venu9WERUVhhWiUjJpknx0OmXH2tOT1+YUhXAY6O2VCbWJBFBVxastE2UZwwpRKfH75ag/GpUd6unTXHp/orq6ZO0afYn9iop8t4io6DCsEJUSux2orpZ5K2639Kxw7sr5i0SAzk7pVenrk54rDgERZR3DClGpqaqSibb6AnHt7dIrQON36pT0UtlscpsyJd8tIipKDCtEpaa8XHpX9Mmg3d1yo/EJhSSsuN0yZ6WyUtZXIaKsY1ghKkVTp8rQRSwmPQJtbby44Xgkk1KzZFK+ttuBmhpOrCUyCMMKUSkqKwPq6iSglJfLGS1tbZxsO1YnT0pvlMcj81amTGGvCpGBGFaIStWUKTJ0EQ7LTvfkSTk7iEbX0yPBzuWSoOL1SvAjIsMwrBCVKpsNaGiQ0231Cxy2tvLsoNH09QFHj8qEZKtVhn2mTZMaEpFhGFaISpnXK4FFv8BhPA4cOZK+gjClxWLp2rjd8rGmRnqniMhQDCtEpW7KFKC2Vs5o0XfChw4xsGSKxYDDh4EzZyTghUJyRtW0aZxUS5QDDCtEpc5ikd6VKVNk/oq+Mz50iFdmBuQU70OHZKVan09qU1EBTJ8uQ2lEZDiGFSKSne706cDkybIz9vmkp+W990p7DZZQKF0Dn0/Cm98PzJzJeSpEOWTPdwOIyCQcDqCpST4/fVp2zv39srOur5eholLpSdA0qcGxYzKPJ7NHpalJTv0mopxhWCGiNIdDeg3sdjmVWb/OTUuL7KynTSv+9UT6+oDjxyWsOBxSg1BIep2mT5dTlokopxhWiGggux2YMUN2yvpCcT6frC8SDsvqt/oKuMUkHpfl89vb5Xo/Xq987OuTnqVp06Q2RJRzfOUR0VBWq+ycPR5ZeyUUkjOFlJJeh85OmZBbVSXbC1k0KpNnOzrkDCiXS4JKOCy9KjNmSK8Kz/ohyhtDJ9h+9KMfRWNjI8rKylBXV4dly5ahra1twGNaWlpwyy23wOPxoLq6Gvfccw9ivEYJkTlMmgRcdJH0LMRiMofF65X7WluB//kf4OBBCS+F9LpNJGTS7OHDwL598jGRkB6keFx6U6ZOlb+9uppBhSjPDO1ZWbx4Mb7xjW+grq4Ox48fx3333YePf/zj2L59OwAgmUxi6dKlmDJlCrZt24bOzk4sX74cSik8+eSTRjaNiMbK6ZS5GpWVwIkTstYIIKFFn4h66pT0SPh8cisvl5vDkdempyQSErR6e6W93d3SowLIcJbfL70q+tk+dXUS1BhSiEzB0LDyta99LfX59OnT8eCDD+LWW29FPB6Hw+HA5s2bsX//frS2tqK+vh4A8Pjjj2PFihVYt24d/H6/kc0jovHw+yWg9PRIOOnpkRDgckmgSSSkh+XUKRlGcjjkPrdbAoHDIXM+7HY5q8hqTS9Zr38ExhYQlErfNC19SybllkhID0k0KiGkv1++Tial12TSJAlVem+RxSJf19TIfaVy1hNRgcjZnJWuri788Ic/xJVXXgnH2aOtHTt2YO7cuamgAgA33ngjotEodu/ejcWLFw/5OdFoFFH9iAhA8Ox1TDRNg6ZpBv8VpUWvKWtrvIKqc0WFBJdwWHoozpyRz5WSIFJWJjv7eFyCQigkQQJIBxM9nAy+DRaPy88dbk2TzMACDAwtmfSAVF4uNY5GofX3S5BxudJzb3w+aZf+s2hCCuo5XcAKuc7jabPhYeWBBx7AU089hUgkgg996EN45ZVXUve1t7ejpqZmwOMrKyvhdDrR3t4+7M/bsGED1q5dO2R7a2srfMV+SmWORSIRhEIhHDt2DO5Cn0RpckopdHd3w2KxwFJoQw9+v4QJ/cyZSER6MICh4cRqTfd86CFD/6jL/Lq3V3o/KirSQUL/uYM/zww8mT0u/f2pn6msVnQnk7C4XLCUl0tYsdmkl6inJ4tFoYJ+TheQQq5zKBQa82PHHVaam5uHDQuZdu3ahfnz5wMAvv71r+Pzn/88jh49irVr1+Kzn/0sXnnllVRRhyuuUmrEoq9ZswarV69OfR0MBhEIBBAIBDhslGXhcBgHDhxAQ0MDvPqkSjKEpmlQSiEQCMBqLeCFpZVKhxb9Y1+fBA59mEY/msr8O5Uaefinv1/CyuD5LyN9jx6ObDb5nrIyGYpyuaC5XFAdHQhMn17YdS4ARfOcNrlCrnNwHFd4H3dYWblyJe64445RHzNjxozU59XV1aiursaFF16I973vfQgEAnjrrbewcOFC1NbWYufOnQO+t7u7G/F4fEiPi87lcsE1zKJMVqu14P5RZqfXlLXNjaKptds99HTmZFKGdBKJ9LySzGGbwb0s+sfqapkQW1srvTeD57Vk9tzoAcVmSw/9DF4XRdNgtdmKo84FoGie0yZXqHUeT3vHHVb08HE+1Nk3IH3OycKFC7Fu3TqcOHECdXV1AIDNmzfD5XJh3rx55/U7iMiE9BBxPmbPzm5biKjgGDZn5e2338bbb7+Nq6++GpWVlTh06BC++c1v4oILLsDChQsBAEuWLMGcOXOwbNkyPProo+jq6sJ9992Hu+66i0M6REREBMDAReHKy8vx8ssv4/rrr8dFF12Ez33uc5g7dy62bt2aGsax2Wx49dVXUVZWhquuugqf+MQncOutt+Kxxx4zqllERERUYAzrWbn00kvx+9///pyPa2xsHHCGEBEREVGmwpqNQ0RERCWHYYWIiIhMjWGFiIiITI1hhYiIiEyNYYWIiIhMjWGFiIiITI1hhYiIiEyNYYWIiIhMjWGFiIiITI1hhYiIiEyNYYWIiIhMjWGFiIiITI1hhYiIiEyNYYWIiIhMjWGFiIiITI1hhYiIiEyNYYWIiIhMjWGFiIiITI1hhYiIiEyNYYWIiIhMjWGFiIiITI1hhYiIiEyNYYWIiIhMjWGFiIiITI1hhYiIiEyNYYWIiIhMjWGFiIiITI1hhYiIiEyNYYWIiIhMjWGFiIiITI1hhYiIiEyNYYWIiIhMjWGFiIiITI1hhYiIiEwtJ2ElGo3i/e9/PywWC/bs2TPgvpaWFtxyyy3weDyorq7GPffcg1gslotmERERUQGw5+KX3H///aivr8ef/vSnAduTySSWLl2KKVOmYNu2bejs7MTy5cuhlMKTTz6Zi6YRERGRyRnes/KrX/0KmzdvxmOPPTbkvs2bN2P//v144YUXcPnll+PDH/4wHn/8cfzgBz9AMBg0umlERERUAAwNKydPnsRdd92F//iP/4Db7R5y/44dOzB37lzU19entt14442IRqPYvXu3kU0jIiKiAmHYMJBSCitWrMCXvvQlzJ8/H0eOHBnymPb2dtTU1AzYVllZCafTifb29mF/bjQaRTQaTX2t98BomgZN07L3B1Cqpqyt8Vjn3GCdc4e1zo1CrvN42jzusNLc3Iy1a9eO+phdu3Zh+/btCAaDWLNmzaiPtVgsQ7YppYbdDgAbNmwY9ve3trbC5/ON+rtofCKRCEKhEI4dOzZszxhlj1IK3d3dsFgsIz73aeJY59xhrXOjkOscCoXG/Nhxh5WVK1fijjvuGPUxM2bMwLe//W289dZbcLlcA+6bP38+Pv3pT+P5559HbW0tdu7cOeD+7u5uxOPxIT0uujVr1mD16tWpr4PBIAKBAAKBAPx+/3j/HBpFOBzGgQMH0NDQAK/Xm+/mFDVN06CUQiAQgNXKFQWMwjrnDmudG4Vc5/HMTR13WKmurkZ1dfU5H/fd734X3/72t1Nft7W14cYbb8SPfvQjLFiwAACwcOFCrFu3DidOnEBdXR0AmXTrcrkwb968YX+uy+UaEoAAwGq1Ftw/yuz0mrK2ucFa5wbrnDusdW4Uap3H017D5qw0NjYO+Fo/Mr/gggvQ0NAAAFiyZAnmzJmDZcuW4dFHH0VXVxfuu+8+3HXXXewlISIiIgB5XsHWZrPh1VdfRVlZGa666ip84hOfwK233jrsac5ERERUmnKyKBwg81iUUkO2NzY24pVXXslVM4iIiKjAFNYAFxEREZUchhUiIiIyNYYVIiIiMjWGFSIiIjI1hhUiIiIyNYYVIiIiMjWGFSIiIjI1hhUiIiIyNYYVIiIiMjWGFSIiIjI1hhUiIiIyNYYVIiIiMjWGFSIiIjK1nF112Sj6lZyDwWCeW1J8wuEwIpEIgsEgNE3Ld3OKmqZpCIVCCAaDsFp5DGEU1jl3WOvcKOQ66/ttfT8+moIPK6FQCAAQCATy3BIiIiIar1AohIqKilEfY1FjiTQmpmka2tra4PP5YLFY8t2cohIMBhEIBNDa2gq/35/v5hQ11jo3WOfcYa1zo5DrrJRCKBRCfX39OXuFCr5nxWq1oqGhId/NKGp+v7/gXgSFirXODdY5d1jr3CjUOp+rR0VXWANcREREVHIYVoiIiMjUGFZoRC6XCw8//DBcLle+m1L0WOvcYJ1zh7XOjVKpc8FPsCUiIqLixp4VIiIiMjWGFSIiIjI1hhUiIiIyNYYVIiIiMjWGFRrR008/jaamJpSVlWHevHl48803892kgrZhwwZcccUV8Pl8mDp1Km699Va8++67Ax6jlEJzczPq6+tRXl6ORYsWYd++fXlqcXHYsGEDLBYLVq1aldrGOmfP8ePH8ZnPfAaTJ0+G2+3G+9//fuzevTt1P2s9cYlEAv/8z/+MpqYmlJeXY+bMmfjWt7414JptRV9nRTSMl156STkcDvWDH/xA7d+/X917773K4/Goo0eP5rtpBevGG29Uzz77rPrLX/6i9uzZo5YuXaoaGxtVOBxOPeaRRx5RPp9P/fSnP1V79+5Vn/zkJ1VdXZ0KBoN5bHnhevvtt9WMGTPUZZddpu69997UdtY5O7q6utT06dPVihUr1M6dO9Xhw4fVb3/7W/Xee++lHsNaT9y3v/1tNXnyZPXKK6+ow4cPqx//+MfK6/WqjRs3ph5T7HVmWKFhffCDH1Rf+tKXBmy7+OKL1YMPPpinFhWfjo4OBUBt3bpVKaWUpmmqtrZWPfLII6nH9Pf3q4qKCvVv//Zv+WpmwQqFQmr27Nlqy5Yt6rrrrkuFFdY5ex544AF19dVXj3g/a50dS5cuVZ/73OcGbLvtttvUZz7zGaVUadSZw0A0RCwWw+7du7FkyZIB25csWYLt27fnqVXFp6enBwBQVVUFADh8+DDa29sH1N3lcuG6665j3c/DV77yFSxduhQf/vCHB2xnnbPnF7/4BebPn4/bb78dU6dOxeWXX44f/OAHqftZ6+y4+uqr8bvf/Q5/+9vfAAB/+tOfsG3bNnzkIx8BUBp1LvgLGVL2nT59GslkEjU1NQO219TUoL29PU+tKi5KKaxevRpXX3015s6dCwCp2g5X96NHj+a8jYXspZdewh//+Efs2rVryH2sc/YcOnQImzZtwurVq/GNb3wDb7/9Nu655x64XC589rOfZa2z5IEHHkBPTw8uvvhi2Gw2JJNJrFu3Dp/61KcAlMZzmmGFRmSxWAZ8rZQaso3Oz8qVK/HnP/8Z27ZtG3If6z4xra2tuPfee7F582aUlZWN+DjWeeI0TcP8+fOxfv16AMDll1+Offv2YdOmTfjsZz+behxrPTE/+tGP8MILL+DFF1/EJZdcgj179mDVqlWor6/H8uXLU48r5jpzGIiGqK6uhs1mG9KL0tHRMSS50/h99atfxS9+8Qu89tpraGhoSG2vra0FANZ9gnbv3o2Ojg7MmzcPdrsddrsdW7duxXe/+13Y7fZULVnniaurq8OcOXMGbHvf+96HlpYWAHxOZ8vXv/51PPjgg7jjjjtw6aWXYtmyZfja176GDRs2ACiNOjOs0BBOpxPz5s3Dli1bBmzfsmULrrzyyjy1qvAppbBy5Uq8/PLL+P3vf4+mpqYB9zc1NaG2tnZA3WOxGLZu3cq6j8P111+PvXv3Ys+ePanb/Pnz8elPfxp79uzBzJkzWecsueqqq4acfv+3v/0N06dPB8DndLZEIhFYrQN31zabLXXqcknUOY+Te8nE9FOXn3nmGbV//361atUq5fF41JEjR/LdtIL15S9/WVVUVKjXX39dnThxInWLRCKpxzzyyCOqoqJCvfzyy2rv3r3qU5/6VFGdfpgvmWcDKcU6Z8vbb7+t7Ha7WrdunTpw4ID64Q9/qNxut3rhhRdSj2GtJ2758uVq2rRpqVOXX375ZVVdXa3uv//+1GOKvc4MKzSif/3Xf1XTp09XTqdTfeADH0idYkvnB8Cwt2effTb1GE3T1MMPP6xqa2uVy+VS1157rdq7d2/+Gl0kBocV1jl7/uu//kvNnTtXuVwudfHFF6vvf//7A+5nrScuGAyqe++9VzU2NqqysjI1c+ZM9dBDD6loNJp6TLHX2aKUUvns2SEiIiIaDeesEBERkakxrBAREZGpMawQERGRqTGsEBERkakxrBAREZGpMawQERGRqTGsEBERkakxrBAREZGpMawQERGRqTGsEBERkakxrBAREZGpMawQERGRqf3/Fej6MhjNyE0AAAAASUVORK5CYII=", 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", 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" ] }, "metadata": {}, "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "my_component: uid a71ccce9, ports [], references ['mzi_1'], 0 polygons\n" - ] } ], "source": [ @@ -358,80 +544,176 @@ "PDK = gf.get_generic_pdk()\n", "PDK.activate()\n", "\n", + "# The << is shorthand for c.add_ref()\n", "c = gf.Component(\"my_component\")\n", - "mzi = c << gf.components.mzi(delta_length=15)\n", + "mzi = c << gf.components.mzi(delta_length=0)\n", "\n", - "c\n" + "c.plot_matplotlib()" ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ - "### Quick check\n", + "There are there three different relevant amplitudes:\n", + "1. The amplitude of the input light:\n", + "$\n", + " I_{input} = E_{input}^2\n", + "$\n", "\n", - "What is the intensity of the output light if the input light had an intensity of 0.75 mW and a refractive index through silicon of $n = 3.48$ when operating at a wavelength of 1450 nm? The shorter path has a length of 90 $\\mu \\text{m}$ and the longer path a length of 102 $\\mu \\text{m}$.\n", + "2. The amplitudes of the beams after the split\n", + "$\n", + " E_{1} = \\frac{E_{input}}{\\sqrt{2}}, \\hspace{2mm} E_{2} = \\frac{E_{input}}{\\sqrt{2}}\n", + "$\n", + "\n", + "3. The amplitude of the recombined light\n", + "$\n", + " I_{output} = [\\frac{E_{1}+E_{2}}{\\sqrt{2}}]^2 = I_{input}\n", + "$\n", + "\n", + "```{warning} \n", + "Note that these are not generalized equations. Instead, these describe a balanced interferometer whose branches have not undergone a phase shift.\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Quick check\n", + "If intensity of the input wave to a balanced interferometer is 0.5 mW, what is the intensity at the output? Assume lossless waveguides.\n", "\n", "
\n", " Answer\n", - " 0.491 mW\n", + " 0.5 mW \n", "
" ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ - "## Thermo-optic effect" + "## Definitions\n", + "\n", + "It would be helpful to mathematically model the light in our waveguides. We can start to build a model by considering the equation for a plane wave.\n", + "\n", + "$$ \n", + "E = E_0 e^{i(\\omega t - \\beta z)}\n", + "$$\n", + "\n", + "Propagation Constant of Light:\n", + "$\n", + " \\beta = \\frac{2 \\pi n}{\\lambda} \n", + "$\n", + "\n", + "If the equations above describe the propogation of the wave before the light gets split by the MZI, we can represent the two resulting beams as follows.\n", + "\n", + "$$\n", + "E_{o1} = \\frac{E_{i}}{\\sqrt{2}}e^{-i\\beta_{1}L_{1}-\\frac{\\alpha_{1}}{2}L_{1}}\n", + "$$\n", + "\n", + "$$\n", + "E_{o2} = \\frac{E_{i}}{\\sqrt{2}}e^{-i\\beta_{2}L_{2}-\\frac{\\alpha_{2}}{2}L_{2}}\n", + "$$\n", + "\n", + "*** As the light travels through the waveguide we can imagine that it will experience some degree of loss. The 'α' term that appeared in the exponents is the loss coefficient. For convenience, the following examples will assume that α = 0, or that there is zero loss in our waveguides.\n", + "\n", + "And finally the intensity of the the recombined light at the end of the MZI can be described like so:\n", + "\n", + "$$\n", + "I_{output} = \\frac{I_{input}}{4} \\lvert E_{o1} + E_{o2} \\lvert ^2 \n", + "$$" ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ - "While a balanced MZI will not experience a phase shift due to a difference in path length, we can introduce a phasae shift by utilizing other methods. The thermo-optic effect describes the effect of heat on the phase of light. By heating up one of the waveguide in the MZI, we can control the phase shift of that waveguide and therefor the intensity of the output. This is a practical way to turn our otherwise static MZI in to a switch that we can control. " + "## Analysis of imblanced MZI's\n", + "\n", + "The equation below is the simplified version of the above expressions assuming no difference in the propogation constants of the two different waveguides and no loss.\n", + "\n", + "$$\n", + "I_{output} = \\frac{I_{input}}{2}(1 + cos(\\beta \\Delta L))\n", + "$$" ] }, { - "attachments": {}, - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 4, "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[32m2024-01-09 22:59:50.499\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mgdsfactory.technology.layer_views\u001b[0m:\u001b[36m__init__\u001b[0m:\u001b[36m785\u001b[0m - \u001b[1mImporting LayerViews from YAML file: '/home/sequoia/git/Photonics-Bootcamp/env/lib/python3.11/site-packages/gdsfactory/generic_tech/layer_views.yaml'.\u001b[0m\n", + "\u001b[32m2024-01-09 22:59:50.508\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mgdsfactory.pdk\u001b[0m:\u001b[36mactivate\u001b[0m:\u001b[36m258\u001b[0m - \u001b[1m'generic' PDK is now active\u001b[0m\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "![Image of a thermo-optic switch](https://raw.githubusercontent.com/BYUCamachoLab/Photonics-Bootcamp/main/book/images/Notebook_Images/thermo_optic_switch.png)" + "import gdsfactory as gf\n", + "\n", + "PDK = gf.get_generic_pdk()\n", + "PDK.activate()\n", + "\n", + "c = gf.Component(\"my_component\")\n", + "mzi = c << gf.components.mzi(delta_length=15)\n", + "\n", + "c.plot_matplotlib()" ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, - "source": [] + "source": [ + "### Quick check\n", + "\n", + "What is the intensity of the output light if the input light had an intensity of 0.75 mW and a refractive index through silicon of $n = 3.48$ when operating at a wavelength of 1450 nm? The shorter path has a length of 90 $\\mu \\text{m}$ and the longer path a length of 102 $\\mu \\text{m}$.\n", + "\n", + "
\n", + " Answer\n", + " 0.491 mW\n", + "
" + ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ - "Introducing a phase shift in this way will affect the propogation constant 'β' and since we are assuming that the path lengths are equal, we can modify our previous equation like this:" + "## Thermo-optic effect\n", + "\n", + "While a balanced MZI will not experience a phase shift due to a difference in path length, we can introduce a phasae shift by utilizing other methods. The thermo-optic effect describes the effect of heat on the phase of light. By heating up one of the waveguide in the MZI, we can control the phase shift of that waveguide and therefor the intensity of the output. This is a practical way to turn our otherwise static MZI in to a switch that we can control. " ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ - "$$\n", - "I_{output} = \\frac{I_{input}}{2}(1 + cos(\\Delta\\beta L))\n", - "$$" + "![Image of a thermo-optic switch](https://raw.githubusercontent.com/BYUCamachoLab/Photonics-Bootcamp/main/book/images/Notebook_Images/thermo_optic_switch.png)" ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ + "Introducing a phase shift in this way will affect the propogation constant 'β' and since we are assuming that the path lengths are equal, we can modify our previous equation like this:\n", + "\n", + "$$\n", + "I_{output} = \\frac{I_{input}}{2}(1 + cos(\\Delta\\beta L))\n", + "$$\n", + "\n", "### Quick check\n", "Assume that the lengths of the two paths are 100µm, the light has a wavelength of 1500nm and the ouptput was measured to be 0.9mW. What is the input intensity of the MZI if the heater introduced a 90° phase shift in the top waveguide?\n", "
\n", @@ -448,7 +730,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "env", "language": "python", "name": "python3" }, @@ -462,7 +744,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.3" + "version": "3.11.5" }, "orig_nbformat": 4 }, diff --git a/_sources/pages/setup.md b/_sources/pages/setup.md index d1c677e..1bec3aa 100644 --- a/_sources/pages/setup.md +++ b/_sources/pages/setup.md @@ -1,27 +1,42 @@ # Setting up the tools -Since we use a lot of python tools in this course, there are some things which you'd need to install. In short: - -1. You'd need to either have a Linux machine, a Mac, or a Windows machine with the Windows Subsystem for Linux (WSL) installed. -2. You'd need a Code Editor. The recommended code editor is VSCode, which you can set up [here](/pages/vscode.md). -3. You'd need Miniconda. Some of the tools are available through pip, while some are only available through Conda. Miniconda is a nice way to manage all these tools in a single place. -4. You'd need KLayout. This is a layout viewer, which we'll use to view the layouts we create. +Since we primarily use Python tools in this course, there are some things +you'll need to install. In short, you'll need: + +1. A Linux machine, a Mac, or a Windows machine with the + [Windows Subsystem for Linux (WSL)](https://learn.microsoft.com/en-us/windows/wsl/about) + installed. +2. A code editor. The recommended code editor is + [Visual Studio Code](https://code.visualstudio.com/), (installation + instructions [here](/pages/vscode.md)). +3. Miniconda. Most of the tools are installable through the + [Python Package Index (PyPI)](https://pypi.org/), i.e. "pip" installable, + while some precompiled packages are only available through Conda + repositories. Miniconda, which also provides a Python installation and + virtual environments by default, is a nice way to manage all these tools + in a single place. +4. KLayout, a layout/GDS file viewer, which we'll use to view the circuits we + create. ## Install WSL -If you are using Windows, you'd have to install WSL. To install: -1. Open Command Prompt as Administrator -2. Type `wsl --install` and press Enter -3. Restart your computer -4. Click on the Start menu and type "Ubuntu" and press Enter -5. Set up your username and password +If you are using Windows, you'll need to install {term}`WSL``. This can be +easily installed through the Windows store (recommended), or via the command +line: + +1. Open Command Prompt as Administrator. +2. Run the command ``wsl --install``. +3. Restart your computer. +4. From the Start menu, run Ubuntu. +5. On the first run, set up a new user account on the Linux machinen (username + and password, which can be different from your Windows machine). ## Install VSCode You now need to install VSCode. Refer to the chapter on VSCode [here](/pages/vscode.md), which explains what VSCode is and how to install it. Open VSCode. You can setup a theme and other things. If you are using WSL, make sure to [open VSCode in your WSL environment](https://code.visualstudio.com/docs/remote/wsl#_open-a-remote-folder-or-workspace). You can check this by checking the green box in the bottom left-hand corner. If VSCode is connected to WSL it will say "WSL" in this box. If it doesn't then click the green box. A menu will pop up. Click the option "Connect to WSL" and VSCode will now be connected to WSL. -## Setup Script + \ No newline at end of file diff --git a/_sources/pages/vscode.md b/_sources/pages/vscode.md index 14567d5..b2dcaa9 100644 --- a/_sources/pages/vscode.md +++ b/_sources/pages/vscode.md @@ -3,7 +3,7 @@ While you can write computer code using any text editor, some text editors have special features that make writing code easier. In this course, we will be using [Visual Studio Code](https://code.visualstudio.com) (also known as -vscode), a popular open-source text editor by Microsoft that is desiged +{term}`vscode`), a popular open-source text editor by Microsoft that is desiged specifically for writing code. It boasts a healthy set of extensions that can provide integrated development environment (IDE)-like capabilities in a much lighter program. diff --git a/genindex.html b/genindex.html index 3b6a78a..e01fda4 100644 --- a/genindex.html +++ b/genindex.html @@ -265,6 +265,7 @@

Index

| P | R | S + | V | W

C

@@ -369,6 +370,13 @@

S

+

V

+ + +

W

    diff --git a/objects.inv b/objects.inv index c65b443..dedd1d4 100644 Binary files a/objects.inv and b/objects.inv differ diff --git a/pages/git_and_github.html b/pages/git_and_github.html index be6eb23..9a533fa 100644 --- a/pages/git_and_github.html +++ b/pages/git_and_github.html @@ -302,7 +302,7 @@

    Git (and GitHub)

    While you can install Git for Windows, because the other software packages used in this course are Mac- or Linux-only, you -will be forced to use WSL to complete this course on Windows. Still, we’ll +will be forced to use git via WSL to complete this course. Still, we’ll provide a download link for git on Windows.

    @@ -332,12 +332,12 @@

    Git (and GitHub)GitHub is the most well known hosting service, and it +provides free accounts (and free private repositories) to all users. This +bootcamp, for example, is hosted on GitHub, along with many of the most popular +open-source Python projects (including numpy, scipy, and matplotlib). If you +want to version control your code, we recommend creating an account on GitHub +and keeping your source code there.

    + @@ -279,6 +282,14 @@

    KLayout

    +
    +

    Contents

    +
    +
    @@ -291,6 +302,17 @@

    KLayoutsource)

    KLayout is a free and open-source software for layout design and verification. It’s most basic use case is as a layout viewer (it can read and display GDS files, the most common format for laying out photonic chips), but it is a powerful tool for designing photonic devices and integrated circuits as well. It has features for DRC, viewing chip cross-sections, tracing nets (to help you detect shorts), and more, while also being scriptable in several languages including Ruby and Python. KLayout is available for Windows, Mac, and Linux. You can download KLayout here.

    +
    +

    klive#

    +

    klive is a small extension to KLayout that allows automatic loading for GDS +files when some external program sends a json request with the gds path to a +klive server, running in the background. This essentially allows for +“hot-reloading” of your layouts within KLayout each time you rerun your code.

    +

    Once KLayout is installed, you can install klive from within KLayout’s package +manager, by going to Tools > Manage Packages > Install New Packages. Then, +search for klive and double click it to select it, then click “Apply”.

    +

    klive installation screenshot

    +