AI-empowered optical metasurfaces: From unit-cell optimization to system-level integration

Optical metasurfaces, composed of subwavelength structures, represent an emerging class of optical devices capable of precisely manipulating electromagnetic wavefronts, enabling unprecedented miniaturization and planarization in optical systems. However, critical challenges remain across different stages from unit-cell design to system-level integration: at the unit-cell level, high computational costs, complex nonlinear design spaces, and sensitivity to fabrication errors impede rapid and robust design; at the metasurface level, precise modeling of inter-unit coupling, balancing multiple conflicting objectives, and achieving efficient real-time dynamic control remain difficult; at the system level, integration complexity, susceptibility to hardware noise and environmental variability, and incompatibility between optical design and backend reconstruction algorithms significantly limit overall performance. Recent advances in artificial intelligence (AI) have provided effective solutions to these issues. Specifically, AI techniques substantially reduce computational overhead, navigate complex nonlinear design spaces, and enhance robustness against manufacturing imperfections at the unit-cell stage; accurately model inter-unit interactions, facilitate efficient multi-objective optimization, and enable real-time dynamic metasurface reconfiguration; and at the system level, integrate optical design and backend reconstruction through end-to-end optimization frameworks, effectively mitigating environmental variability, hardware noise, and design-reconstruction incompatibility. This review systematically summarizes recent progress in AI-driven optical metasurface research, highlighting a clear trend from isolated component optimization toward integrated, end-to-end system-level optimization. All data are available in the main text or the supplementary materials from the corresponding author upon reasonable request.