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Nature communications
Aug 06, 2024;15 (1): 6686.

Terahertz spoof plasmonic neural network for diffractive information recognition and processing.

Xinxin Gao, Ze Gu, Qian Ma, Bao Jie Chen, Kam-Man Shum, Wen Yi Cui, Jian Wei You, Tie Jun Cui, Chi Hou Chan
Author Information
  1. Xinxin Gao: State Key Laboratory of Terahertz and Millimeter Waves, City University of Hong Kong, Hong Kong, China. ORCID
  2. Ze Gu: State Key Laboratory of Millimeter Waves, Southeast University, Nanjing, China.
  3. Qian Ma: State Key Laboratory of Millimeter Waves, Southeast University, Nanjing, China. maqian@seu.edu.cn. ORCID
  4. Bao Jie Chen: State Key Laboratory of Terahertz and Millimeter Waves, City University of Hong Kong, Hong Kong, China.
  5. Kam-Man Shum: State Key Laboratory of Terahertz and Millimeter Waves, City University of Hong Kong, Hong Kong, China. ORCID
  6. Wen Yi Cui: State Key Laboratory of Millimeter Waves, Southeast University, Nanjing, China. ORCID
  7. Jian Wei You: State Key Laboratory of Millimeter Waves, Southeast University, Nanjing, China. ORCID
  8. Tie Jun Cui: State Key Laboratory of Millimeter Waves, Southeast University, Nanjing, China. tjcui@seu.edu.cn. ORCID
  9. Chi Hou Chan: State Key Laboratory of Terahertz and Millimeter Waves, City University of Hong Kong, Hong Kong, China. eechic@cityu.edu.hk. ORCID
PMID: 39107313 DOI: 10.1038/s41467-024-51210-2

Abstract

All-optical diffractive neural networks, as analog artificial intelligence accelerators, leverage parallelism and analog computation for complex data processing. However, their low space transmission efficiency or large spatial dimensions hinder miniaturization and broader application. Here, we propose a terahertz spoof plasmonic neural network on a planar diffractive platform for direct multi-target recognition. Our approach employs a spoof surface plasmon polariton coupler array to construct a diffractive network layer, resulting in a compact, efficient, and easily integrable architecture. We designed three schemes: basis vector classification, multi-user recognition, and MNIST handwritten digit classification. Experimental results reveal that the terahertz spoof plasmonic neural network successfully classifies basis vectors, recognizes multi-user orientation information, and directly processes handwritten digits using a designed input framework comprising a metal grating array, transmitters, and receivers. This work broadens the application of terahertz plasmonic metamaterials, paving the way for terahertz on-chip integration, intelligent communication, and advanced computing systems.

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