Data-driven concurrent nanostructure optimization based on conditional generative adversarial networks.

ABSTRACT: Iterative numerical optimization is a ubiquitous tool to design optical nanostructures. However, there can be a significant performance gap between the numerically simulated results, with pristine shapes, and the experimentally measured values, with deformed profiles. We introduce conditional generative adversarial networks (CGAN) into the standard iterative optimization loop to learn process-structure relationships and produce realistic simulation designs based on the fabrication conditions. This ensures that the process-structure mapping is accurate for the specific available equipment and moves the optimization space from the structural parameters (e.g. width, height, and period) to process parameters (e.g. deposition rate and annealing time). We demonstrate this model agnostic optimiza

SUBMITTER: Baucour A 

PROVIDER: S-EPMC11501161 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature