Nanophotonic Particle Simulation and Inverse Design Using Artificial Neural Networks

TL;DR

This paper introduces a neural network-based approach for fast and precise simulation of light scattering in multilayer nanoparticles, enabling efficient inverse design in nanophotonics.

Contribution

It demonstrates that neural networks can accurately approximate complex nanophotonic simulations with minimal training data and facilitate inverse design through analytical gradients.

Findings

Neural networks achieve high-precision approximation with limited training data.

Simulation speed is significantly faster than traditional methods.

Enables efficient inverse design via backpropagation.

Abstract

We propose a method to use artificial neural networks to approximate light scattering by multilayer nanoparticles. We find the network needs to be trained on only a small sampling of the data in order to approximate the simulation to high precision. Once the neural network is trained, it can simulate such optical processes orders of magnitude faster than conventional simulations. Furthermore, the trained neural network can be used solve nanophotonic inverse design problems by using back- propogation - where the gradient is analytical, not numerical.