Investigation of inverse design of multilayer thin-films with conditional invertible Neural Networks

TL;DR

This paper demonstrates that conditional invertible neural networks can effectively generate and refine multilayer thin-film designs for optical targets, outperforming existing methods and handling out-of-distribution cases.

Contribution

The work introduces the use of cINNs for inverse design of multilayer thin-films, enabling stochastic proposal generation and improved target accuracy without retraining.

Findings

cINNs generate diverse thin-film proposals close to targets

Refinement with local optimization improves precision significantly

cINNs can predict designs for out-of-distribution targets

Abstract

The task of designing optical multilayer thin-films regarding a given target is currently solved using gradient-based optimization in conjunction with methods that can introduce additional thin-film layers. Recently, Deep Learning and Reinforcement Learning have been been introduced to the task of designing thin-films with great success, however a trained network is usually only able to become proficient for a single target and must be retrained if the optical targets are varied. In this work, we apply conditional Invertible Neural Networks (cINN) to inversely designing multilayer thin-films given an optical target. Since the cINN learns the energy landscape of all thin-film configurations within the training dataset, we show that cINNs can generate a stochastic ensemble of proposals for thin-film configurations that that are reasonably close to the desired target depending only on random variables. By refining the proposed configurations further by a local optimization, we show that the generated thin-films reach the target with significantly greater precision than comparable state-of-the art approaches. Furthermore, we tested the generative capabilities on samples which are outside the training data distribution and found that the cINN was able to predict thin-films for out-of-distribution targets, too. The results suggest that in order to improve the generative design of thin-films, it is instructive to use established and new machine learning methods in conjunction in order to obtain the most favorable results.