Affiliation:
1. Department of Microwave Engineering School of Electronics and Information Engineering Harbin Institute of Technology Harbin 150001 China
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.
Funder
National Natural Science Foundation of China
Natural Science Foundation of Heilongjiang Province
Subject
Condensed Matter Physics,Atomic and Molecular Physics, and Optics,Electronic, Optical and Magnetic Materials
Cited by
3 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献