Neural Network‐Assisted End‐to‐End Design for Full Light Field Control of Meta‐Optics

Abstract

Abstract Meta‐optics, with unique light‐matter interactions and extensive design space, underpins versatile and compact optical devices through flexible multi‐parameter light field control. However, conventional designs struggle with the intricate interdependencies of nano‐structural complex responses across wavelengths and polarizations at a system level, hindering high‐performance full‐light field control. Here, a neural network‐assisted end‐to‐end design framework that facilitates global, gradient‐based optimization of multifunctional meta‐optics layouts for full light field control is proposed. Its superiority over separated design is showcased by utilizing the limited design space for multi‐wavelength‐polarization holography with enhanced performance (e.g., ≈6 × structural similarity index experimentally). By harnessing the dispersive full‐parameter Jones matrix, orthogonal tri‐polarization multi‐wavelength‐depth holography is further demonstrated, breaking conventional channel limitations. To highlight its versatility, non‐orthogonal polarizations (>3) are showcased for arbitrary polarized‐spectral multi‐information processing applications in display, imaging, and computing. The comprehensive framework elevates light field control in meta‐optics, delivering superior performance, enhanced functionality, and improved reliability, thereby paving the way for next‐generation intelligent optical technologies.