Empowering Metasurfaces with Inverse Design: Principles and Applications

Abstract

Conventional human-driven methods face limitations in designing complex functional metasurfaces. Inverse design is poised to empower metasurface research by embracing fast-growing artificial intelligence. In recent years, many research efforts have been devoted to enriching inverse design principles and applications. In this perspective, we review most commonly used metasurface inverse design strategies including topology optimization, evolutionary optimization, and machine learning techniques. We elaborate on their concepts and working principles, as well as examples of their implementations. We also discuss two emerging research trends: scaling up inverse design for large-area aperiodic metasurfaces and end-to-end inverse design that co-optimizes photonic hardware and post-image processing. Furthermore, recent demonstrations of inverse-designed metasurfaces showing great potential in real-world applications are highlighted. Finally, we discuss challenges in future inverse design advancement, suggest potential research directions, and outlook opportunities for implementing inverse design in nonlocal metasurfaces, reconfigurable metasurfaces, quantum optics, and nonlinear metasurfaces.