Progressive-Growing of Generative Adversarial Networks for Metasurface Optimization

TL;DR

This paper introduces a progressive training method for GANs to generate high-performance metasurfaces with detailed features, reducing the need for costly design refinement and matching the quality of gradient-based optimization.

Contribution

The paper presents a novel progressive training approach for GANs that improves the generation of complex metasurface designs, outperforming basic GAN architectures.

Findings

Generated metasurfaces match top gradient-based designs in performance

Progressive training captures fine geometric details effectively

Reduces computational cost of metasurface design process

Abstract

Generative adversarial networks, which can generate metasurfaces based on a training set of high performance device layouts, have the potential to significantly reduce the computational cost of the metasurface design process. However, basic GAN architectures are unable to fully capture the detailed features of topologically complex metasurfaces, and generated devices therefore require additional computationally-expensive design refinement. In this Letter, we show that GANs can better learn spatially fine features from high-resolution training data by progressively growing its network architecture and training set. Our results indicate that with this training methodology, the best generated devices have performances that compare well with the best devices produced by gradient-based topology optimization, thereby eliminating the need for additional design refinement. We envision that this network training method can generalize to other physical systems where device performance is strongly correlated with fine geometric structuring.