A Deep Learning Approach for Objective-Driven All-Dielectric Metasurface Design

TLDR

Metasurfaces enable manipulation of optical wavefronts in flat, high‑performance devices, yet conventional design relies on trial‑and‑error, demanding extensive exploration of countless meta‑atom structures. The paper introduces a deep‑learning modeling approach that improves both speed and accuracy in characterizing subwavelength optical structures. The neural‑network approach overcomes dimensional‑mismatch and phase‑prediction challenges, and, when combined with optimization algorithms, can be generically applied across the electromagnetic spectrum. The method is the first neural network to characterize 3‑D dielectric structures and demonstrates on‑demand design of meta‑atoms, metasurface filters, and phase‑change reconfigurable metasurfaces.

Abstract

Metasurfaces have become a promising means for manipulating optical wavefronts in flat and high-performance optical devices. Conventional metasurface device design relies on trial-and-error methods to obtain target electromagnetic (EM) responses, an approach that demands significant efforts to investigate the enormous number of possible meta-atom structures. In this paper, a deep learning modeling approach is introduced that significantly improves on both speed and accuracy compared to techniques currently used to characterize the subwavelength optical structures. Our neural network approach overcomes two key challenges that have limited previous neural-network-based design schemes: input/output vector dimensional mismatch and accurate EM-wave phase prediction. Additionally, this is the first neural network to characterize 3-D dielectric structures. By combining with optimization algorithms or neural networks, this approach can be generically applied to a wide variety of metasurface device designs across the entire electromagnetic spectrum. Using this new methodology, examples of neural networks capable of producing on-demand designs for meta-atoms, metasurface filters, and phase-change reconfigurable metasurfaces are demonstrated.