An Agent-Driven, Cross-Validated Inverse-Design Pipeline for Dielectric Metasurfaces

Abstract

Inverse design of dielectric metasurfaces is powerful but labour-intensive: each new target requires choosing a parameterization and a figure of merit, managing compute resources, and critically assessing results that may be numerically unconverged, sensitive to fabrication errors or trapped in local optima. Here we present an open-source pipeline in which an artificial intelligence (AI) coding agent turns a plain-language specification into metasurface optimization runs, executes them, analyses every run with a fixed checklist and proposes the next iteration. Designs are optimized with differentiable three-dimensional rigorous coupled-wave analysis (RCWA), including robustness to fabrication tolerances, and every reported design is independently verified with finite-difference time-domain (FDTD) simulations. We demonstrate the workflow at 1550 nm on a freeform silicon metagrating, on a polarization-independent meta-atom library assembled into beam-deflecting supercells, and on metalenses built from the same library. In the metagrating case, the independent FDTD verification revealed a 5\% overestimate of the efficiency by the optimization model that a conventional convergence test had not detected, and the robust design retained most of its efficiency under $\pm$10~nm fabrication errors. In the other cases, instead, the obtained RCWA results are confirmed by full-wave simulations.