Deep‐Learning‐Enabled Inverse Design of Large‐Scale Metasurfaces With Full‐Wave Accuracy

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 that maps target near‐field responses to metasurface geometries in a non‐iterative and scalable manner. A lightweight forward prediction network, incorporated 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 (> 20 kλ), with near‐field responses discrepancies less than 3% compared to full‐wave solvers at subwavelength (< λ⁄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.