  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.
