Physics‐Informed Inverse Design of Programmable Metasurfaces

Abstract

Abstract Emerging reconfigurable metasurfaces offer various possibilities for programmatically manipulating electromagnetic waves across spatial, spectral, and temporal domains, showcasing great potential for enhancing terahertz applications. However, they are hindered by limited tunability, particularly evident in relatively small phase tuning over 270°, due to the design constraints with time‐intensive forward design methodologies. Here, a multi‐bit programmable metasurface is demonstrated capable of terahertz beam steering facilitated by a developed physics‐informed inverse design (PIID) approach. Through integrating a modified coupled mode theory (MCMT) into residual neural networks, the PIID algorithm not only significantly increases the design accuracy compared to conventional neural networks but also elucidates the intricate physical relations between the geometry and the modes. Without decreasing the reflection intensity, the method achieves the enhanced phase tuning as large as 300°. Additionally, the inverse‐designed programmable beam steering metasurface is experimentally validated, which is adaptable across 1‐bit, 2‐bit, and tri‐state coding schemes, yielding a deflection angle up to 68° and broadened steering coverage. The demonstration provides a promising pathway for rapidly exploring advanced metasurface devices, with potentially great impact on communication and imaging technologies.

Keywords

• beam steering
• deep learning
• inverse design
• programmable metasurfaces