Optical Neuromorphic Computing Based on Reconfigurable Excitonic Devices.

Journal: Nano letters
Published Date: Dec 30, 2025

Abstract

Optical neuromorphic computing offers promising avenues for real-time image processing and low-power artificial intelligence. Here, we introduce and experimentally validate a fundamentally new computing paradigm that exploits optical exciton dynamics in two-dimensional van der Waals heterostructures. The type-II band alignment and high permittivity (ε ≈ 19) enable exciton control, achieving an exciton-to-trion ratio of ∼7 under electric field modulation at room temperature. Quasi-linear trion photoluminescence acts as an optical synaptic response, with weights dynamically tuned by substrate voltage. By leveraging these programmable optical responses, we have achieved neuromorphic functions, including convolutional filtering for image denoising and fully connected networks for pattern recognition, achieving a classification accuracy rate of 98.7% even under noisy conditions. This work establishes β-TeO2 as a key material for optical neural networks and adaptive vision systems, redefining intelligent photonic processing.

Authors

  • Zhihan Jin
    School of Integrated Circuit Science and Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210023, China.
  • Hao Liu
    Key Laboratory of Development and Maternal and Child Diseases of Sichuan Province, Department of Pediatrics, Sichuan University, Chengdu, China.
  • Tianhong Chen
    School of Electronic Science and Engineering, Nanjing University, Nanjing 210093, China.
  • Tianci Huang
    School of Integrated Circuit Science and Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210023, China.
  • FeiFan Xu
    Hangzhou Dianzi University, School of Computer Science and Technology, HangZhou, ZheJiang, China. Electronic address: [email protected].
  • Chuanqi Tang
    School of Design, South China University of Technology, Guangzhou, 510006, China.
  • Huabin Sun
    College of Integrated Circuit Science and Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210023, China.
  • Chee Leong Tan
    College of Integrated Circuit Science and Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210023, China.
  • Yi Shi
    College of Food Science and Engineering, Nanjing University of Finance and Economics/Collaborative Innovation Center for Modern Grain Circulation and Safety, Nanjing 210023, People's Republic of China.
  • Xiang Wan
    Institute of Computational and Theoretical Study and Department of Computer Science, Hong Kong Baptist University, Hong Kong, P.R. China.
  • Shancheng Yan
    School of Integrated Circuit Science and Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210023, China.

Keywords

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