Physics-data-driven intelligent optimization for large-aperture metalenses

Abstract

Metalenses have gained significant attention and have been widely utilized in optical systems for focusing and imaging, owing to their lightweight, high-integration, and exceptional-flexibility capabilities. Traditional design methods neglect the coupling effect between adjacent meta-atoms, thus harming the practical performance of meta-devices. The existing physical/data-driven optimization algorithms can solve the above problems, but bring significant time costs or require a large number of data-sets. Here, we propose a physics-data-driven method employing an “intelligent optimizer” that enables us to adaptively modify the sizes of the meta-atom according to the sizes of its surrounding ones. The implementation of such a scheme effectively mitigates the undesired impact of local lattice coupling, and the proposed network model works well on thousands of data-sets with a validation loss of 3×10−3. Based on the “intelligent optimizer”, a 1-cm-diameter metalens is designed within 3 hours, and the experimental results show that the 1-mm-diameter metalens has a relative focusing efficiency of 93.4% (compared to the ideal focusing efficiency) and a Strehl ratio of 0.94. Compared to previous inverse design method, our method significantly boosts designing efficiency with five orders of magnitude reduction in time. More generally, it may set a new paradigm for devising large-aperture meta-devices.

Keywords

• intelligence method
• physics-data-driven method
• inverse design
• large-aperture metalenses