From performance to structure: a comprehensive survey of advanced metasurface design for next-generation imaging

Abstract This review introduces a ‘from performance to structure’ imaging metasurface design paradigm, which starts with essential imaging specifications and translates them into corresponding electromagnetic requirements. These requirements are then mapped onto specialized metasurface microstructures, ensuring precise electromagnetic response control. Artificial intelligence (AI) serves as a unifying thread by accelerating inverse design through efficient navigation of high-dimensional parameter spaces and by enhancing imaging performance via AI-driven computational reconstruction algorithms. We synthesize the remarkable performance of metasurfaces across six electromagnetic response control methods in nine imaging domains and categorize three main design approaches—physically-driven, meta-heuristic, and AI-driven methods—while providing a detailed analysis of six primary encoding and decoding strategies and eight common AI techniques. Additionally, nine prospective research directions are highlighted. The review emphasizes that future metasurface imaging systems will leverage electromagnetic response control to link performance with metasurface structure, with AI technology playing a central role in this process.