Investigation of inverse design of multilayer thin-films with conditional invertible neural networks

In this work, we apply conditional invertible neural networks (cINN) to inversely design multilayer thin-films given an optical target in order to overcome limitations of state-of-the-art optimization approaches. Usually, state-of-the-art algorithms depend on a set of carefully chosen initial thin-film parameters or employ neural networks which must be retrained for every new application. We aim to overcome those limitations by training the cINN to learn the loss landscape of all thin-film configurations within a training dataset. We show that cINNs can generate a stochastic ensemble of proposals for thin-film configurations 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 of 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.