Deep-learning-enabled inverse design of large-scale metasurfaces with full-wave accuracy

TL;DR

This paper introduces a deep learning framework for the rapid, accurate inverse design of large-scale metasurfaces, overcoming computational challenges and capturing mutual coupling effects, validated through experimental demonstrations.

Contribution

It presents a scalable, non-iterative inverse design method integrating a surrogate full-wave predictor for large, complex metasurfaces with experimental validation.

Findings

Achieves <3% response discrepancy compared to full-wave simulations.

Enables inverse design of metasurfaces larger than 20,000 wavelengths.

Operates efficiently without high-performance computing resources.

Abstract

Recent advances in meta-optics have enabled diverse functionalities in compact optical devices; however, conventional forward design approaches become inadequate as device complexity and scale grow. Inverse design offers a powerful alternative but often requires massive computational resources and neglects mutual coupling effects. Here, we propose and experimentally validate a deep-learning-enabled framework for rapid inverse design of large-scale, aperiodic metasurfaces with full-wave accuracy.The framework integrates an inverse design network responsible that maps target near-field responses to metasurface geometries in a non-iterative and scalable manner. A lightweight forward prediction network, integrated as a full-wave solver surrogate within the framework, enables efficient end-to-end training of the inverse design network while capturing mutual coupling effects by considering both local and neighboring geometries.The framework's effectiveness is experimentally verified through a multi-foci metalens and a holographic metasurface. This framework enables the inverse design from micrometer to centimeter scales (> 20k{\lambda}), with near-field responses discrepancies less than 3% compared to full-wave solvers at subwavelength (< {\lambda}/10) resolution.Moreover, it is generalizable to metasurfaces of arbitrary size and operates efficiently without high-performance resources, overcoming the computational bottlenecks of previous inverse design methods.