Optical Inverse Design with Automatic Differentiation

This repository contains the code and notebooks for the workshop on the inverse design of optical devices with automatic differentiation.

Getting started

To get started using these course materials, you can transfer them to your local computing environment either by downloading them or git cloning this repository. To download a zip archive of these materials, simply click the green "Clone or download" button on the top right corner of the GitHub repository page and then selecting "Download ZIP" from the drop down menu.

If you have git installed, you can enter the following command in a terminal to clone the repository:

git clone https://github.com/fancompute/workshop-invdesign.git

You will also need a python environment with the necessary packages installed. There are several ways to setup a python environment, but we recommend using Anaconda. You can download the Anaconda installer for your operating system from: https://www.anaconda.com/distribution/.

Note: you will want to download the distribution for a 3.X version of Python (e.g. 3.6 or 3.7).

By default, the Anaconda distribution includes many of the python packages that we require for this workshop. However, there are a few additional packages we will need to install manually, as described in the next sections of this guide.

Installing HIPS autograd

HIPS autograd is an automatic differentiation framework with a Numpy-like API. We rely on autograd for flexible gradient computation in our inverse design studies. To install autograd, execute the following command in your conda environment:

pip install autograd

Installing pymkl

pymkl provides an interface to the PARDISO sparse solver which we use to accelerate our simulations. We have observed approximately a 10X speed up in certain cases compared to the standard SciPy sparse linear solver routines. To install pymkl, execute the following command in your conda environment:

pip install pymkl

Installing ceviche

Ceviche is our research group's finite difference frequency domain (FDFD) and time domain (FDTD) package. The source code is freely available on GitHub at https://github.com/twhughes/ceviche. You can install ceviche by git cloning the repository into your local computing environment and adding its location to your PYTHONPATH environment variable. However, we recommend installing ceviche via pip, by executing the following command:

pip install ceviche

Using the materials

The materials in this repository are organized into Jupyter notebooks. You can interact with them through the jupyter lab interface, which is a web-based user interface that runs from your local computing environment.

References

For more information and further reading on the concepts covered in this workshop, see our group's papers.

• T. W. Hughes, I. A. D. Williamson, M. Minkov, and S. Fan, "Forward-mode Differentiation of Maxwell’s Equations," ACS Photonics, Oct. 2019. doi:10.1021/acsphotonics.9b01238

• T. W. Hughes*, M. Minkov*, I. A. D. Williamson, and S. Fan, "Adjoint Method and Inverse Design for Nonlinear Nanophotonic Devices," ACS Photonics, Dec. 2018. doi:10.1021/acsphotonics.8b01522