Polarization-selective unidirectional and bidirectional diffractive neural networks for information security and sharing.

Journal: Nature communications
Published Date: May 14, 2025

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.

Authors

  • Ziqing Guo
    Terahertz Technology Innovation Research Institute, University of Shanghai for Science and Technology, Shanghai, China.
  • Zhiyu Tan
    Key Laboratory of Green Printing, CAS Research/Education Center for Excellence in Molecular Sciences, Institute of Chemistry, Chinese Academy of Sciences, Beijing 100190, PR China.
  • Xiaofei Zang
    Terahertz Technology Innovation Research Institute, University of Shanghai for Science and Technology, Shanghai, China. xfzang@usst.edu.cn.
  • Teng Zhang
    College of Veterinary Medicine, Hebei Agricultural University, Baoding, Hebei 071000, China.
  • Guannan Wang
    Department of Biological Sciences, Louisiana State University, Baton Rouge, LA, USA.
  • Hongguang Li
    a School of Information Science and Technology , Beijing University of Chemical Technology , Beijing , China.
  • Yuanbo Wang
    Shanghai Key Laboratory of Power Station Automation Technology, School of Mechatronics Engineering and Automation, Shanghai University, Shanghai 200444, China.
  • Yiming Zhu
    Department of Gynecological Oncology, Zhejiang Cancer Hospital, Hangzhou, Zhejiang, 310022, China. Electronic address: zym48306@163.com.
  • Fei Ding
    Information Processing and Communication Technology Lab, Shanghai Institute of Satellite Engineering, Shanghai, China.
  • Songlin Zhuang
    Engineering Research Center of Optical Instrument and System, the Ministry of Education, Shanghai Key Laboratory of Modern Optical System, University of Shanghai for Science and Technology, Shanghai 200093, China.

Keywords

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