Achieving Functional Meta‐Devices by Generalized Meta‐Atom Model for Metasurfaces and Genetic Algorithm

Abstract Built by machine‐learning, the surrogate model of metasurfaces reduces the need for a huge number of simulations in the design process, enhancing the efficiency and performance of the designed meta‐devices. However, the surrogate model of metasurfaces is often constructed‐based on specific physical perspectives or experiences, which limits its versatility. In this study, a generalized surrogate meta‐atom model for metasurfaces is introduced. This model can simulate arbitrary meta‐atoms and their corresponding electromagnetic responses at any polarization within the full space of pixelated unit cells. Utilizing a genetic algorithm, the model is employed to design various types of meta‐devices, automatically generating configurations of meta‐atoms with optimal performance for specific application scenarios. Three typical meta‐devices, including the reflective linear‐circular polarization converter, the metasurface‐based absorber, and the asymmetrical transmission meta‐slab, are designed and validated through full‐wave simulations and/or experiments. This work presents an efficient and flexible approach to model arbitrary metasurfaces, opening new possibilities for metasurface design and applications.