Polarization-selective unidirectional and bidirectional diffractive neural networks for information security and sharing
Ziqing Guo, Zhiyu Tan, Xiaofei Zang, Teng Zhang, Guannan Wang, Hongguang Li, Yuanbo Wang, Yiming Zhu, Fei Ding, Songlin Zhuang
Abstract
Information security aims to protect confidentiality and prevent information leakage, which inherently conflicts with the goal of information sharing. Balancing these competing requirements is especially challenging in all-optical systems, where efficient data transmission and rigorous security are essential. Here we propose and experimentally demonstrate a metasurface-based approach that integrates phase manipulation, polarization conversion, as well as direction- and polarization-selective functionalities into all-optical diffractive neural networks (DNNs). This approach enables a polarization-controllable switch between unidirectional and bidirectional DNNs, thus simultaneously realizing information security and sharing. A cascaded terahertz metasurface comprising quarter-wave plates and metallic gratings is designed to function as a polarization-selective unidirectional-bidirectional classifier and imager. By introducing half-wave plates into a cascade metasurface, we achieve a polarization-controlled transition in unidirectional-bidirectional-unidirectional modes for classification and imaging. Furthermore, we demonstrate a high-security data exchange framework based on these polarization-selective DNNs. The proposed DNNs with polarization-switchable unidirectional/bidirectional capabilities offer significant potential for privacy protection, encryption, communications, and data exchange.