End-to-end design of metasurface-based complex-amplitude holograms by physics-driven deep neural networks.

ABSTRACT: Holograms which reconstruct the transverse profile of light with complex-amplitude information have demonstrated more excellent performances with an improved signal-to-noise ratio compared with those containing amplitude-only and phase-only information. Metasurfaces have been widely utilized for complex-amplitude holograms owing to its capability of arbitrary light modulation at a subwavelength scale which conventional holographic devices cannot achieve. However, existing methods for metasurface-based complex-amplitude hologram design employ single back-diffraction propagation and rely on the artificial blocks which are able to independently and completely control both amplitude and phase. Here, we propose an unsupervised physics-driven deep neural network for the design of metasurface-bas

SUBMITTER: Wei W 

PROVIDER: S-EPMC11501633 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature