Matrix Diffractive Deep Neural Networks Merging Polarization into Meta‐Devices

Abstract

AbstractThe all‐optical diffractive deep neural networks (D2NNs) framework as a hardware platform is demonstrated to implement various advanced functional meta‐devices with high parallelism and high processing speed. However, the design methodology merging trainable polarization modulation neurons into the D2NNs, which potentially possess higher integration and more task‐loading capacity, is not yet fully explored. Here, the matrix diffractive deep neural networks (M‐D2NNs) are proposed to deploy polarization‐sensitive Jones matrix metasurfaces into the all‐optical polarization multiplexing networks to perform sophisticated inference tasks as well as inverse designs for advanced functional meta‐devices. Three polarization multiplexing meta‐devices with advanced functionalities are implemented by the M‐D2NNs, that is, high task‐capacity integration classification, non‐interleaved high‐efficiency Jones matrix eight‐channel regulation, and custom‐polarization information cryptographic multiplexing. The M‐D2NNs are demonstrated to provide a new strategy to merge polarization into electromagnetic and optical field modulators by Jones matrix metasurfaces, which may drive the evolution of all‐optical networks toward multi‐task integration and more advanced functional devices.