Integrated Polarization, Distance, and Rotation for Multi‐DoF Diffractive Processor and Information Encryption
Teng Zhang, Xiaofei Zang, Zhiyu Tan, Guannan Wang, Ziqing Guo, Zhe Gao, A. P. Shkurinov, Fei Ding, Yiming Zhu, Songlin Zhuang
Abstract
Abstract All‐optical diffractive deep neural networks (D 2 NNs) offer significant advantages in processing speed and power consumption, thereby accelerating the development of optical computing and artificial intelligence (AI). Integrating multiple degrees of freedom (multi‐DoF) into D 2 NNs is a pivotal role in improving information processing and task‐loading capacity, an enormous challenge in current all‐optical diffractive computing/processors. Here, a multi‐DoF diffractive processor is proposed and experimentally demonstrated that leverages a metasurfaces‐based approach to integrate polarization, distance, and rotation channels for versatile inference tasks and information encryption. The approach is validated using three‐layer metasurfaces that enable high task‐capacity tasks, including single‐/dual‐digit and single‐/dual‐fashion‐product classification, logic operators, and image transformation. Moreover, by mapping large volumes of input data into multi‐DoF channels and encoding the information in Morse code with our D 2 NNs framework, a high‐security information transmission system is experimentally implemented. The integration of polarization, distance, and rotation channels into an all‐optical diffractive processor with multifunctional capabilities paves the way for multifunctional integrated devices and communication.