A Reinforcement learning method for Optical Thin-Film Design

TL;DR

This paper introduces an end-to-end reinforcement learning-based algorithm that automates the design of optical thin-films, including material selection and spectral optimization, without human intervention.

Contribution

It presents a novel combination of unsupervised learning, reinforcement learning, and genetic algorithms for automated optical thin-film inverse design.

Findings

Successfully optimized spectra of multi-layer solar absorbers

Demonstrated automated material and structure search

Achieved design without human intervention

Abstract

Machine learning, especially deep learning, is dramatically changing the methods associated with optical thin-film inverse design. The vast majority of this research has focused on the parameter optimization (layer thickness, and structure size) of optical thin-films. A challenging problem that arises is an automated material search. In this work, we propose a new end-to-end algorithm for optical thin-film inverse design. This method combines the ability of unsupervised learning, reinforcement learning(RL) and includes a genetic algorithm to design an optical thin-film without any human intervention. Furthermore, with several concrete examples, we have shown how one can use this technique to optimize the spectra of a multi-layer solar absorber device.