A mixture-density-based tandem optimization network for on-demand inverse design of thin-film high reflectors.

ABSTRACT: Deep learning (DL) has emerged as a promising tool for photonic inverse design. Nevertheless, despite the initial success in retrieving spectra of modest complexity with nearly instantaneous readout, DL-assisted design methods often underperform in accuracy compared with advanced optimization techniques and have not proven competitive in handling spectra of practical usefulness. Here, we introduce a tandem optimization model that combines a mixture density network (MDN) and a fully connected (FC) network to inversely design practical thin-film high reflectors. The multimodal nature of the MDN gives access to infinite candidate designs described by probability distributions, which are iteratively sampled and evaluated by the FC network to allow for rapid optimization. We show that the propo

SUBMITTER: Unni R 

PROVIDER: S-EPMC9651023 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature