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A two-dimensional mid-infrared optoelectronic retina enabling simultaneous perception and encoding

Author

Listed:
  • Fakun Wang

    (Nanyang Technological University)

  • Fangchen Hu

    (Nanyang Technological University
    Fudan University)

  • Mingjin Dai

    (Nanyang Technological University)

  • Song Zhu

    (Nanyang Technological University)

  • Fangyuan Sun

    (Nanyang Technological University)

  • Ruihuan Duan

    (Nanyang Technological University)

  • Chongwu Wang

    (Nanyang Technological University)

  • Jiayue Han

    (Nanyang Technological University)

  • Wenjie Deng

    (Nanyang Technological University)

  • Wenduo Chen

    (Nanyang Technological University)

  • Ming Ye

    (Nanyang Technological University)

  • Song Han

    (Nanyang Technological University)

  • Bo Qiang

    (Nanyang Technological University)

  • Yuhao Jin

    (Nanyang Technological University)

  • Yunda Chua

    (Nanyang Technological University)

  • Nan Chi

    (Fudan University)

  • Shaohua Yu

    (Peng Cheng Laboratory)

  • Donguk Nam

    (Nanyang Technological University)

  • Sang Hoon Chae

    (Nanyang Technological University)

  • Zheng Liu

    (Nanyang Technological University)

  • Qi Jie Wang

    (Nanyang Technological University
    Nanyang Technological University)

Abstract

Infrared machine vision system for object perception and recognition is becoming increasingly important in the Internet of Things era. However, the current system suffers from bulkiness and inefficiency as compared to the human retina with the intelligent and compact neural architecture. Here, we present a retina-inspired mid-infrared (MIR) optoelectronic device based on a two-dimensional (2D) heterostructure for simultaneous data perception and encoding. A single device can perceive the illumination intensity of a MIR stimulus signal, while encoding the intensity into a spike train based on a rate encoding algorithm for subsequent neuromorphic computing with the assistance of an all-optical excitation mechanism, a stochastic near-infrared (NIR) sampling terminal. The device features wide dynamic working range, high encoding precision, and flexible adaption ability to the MIR intensity. Moreover, an inference accuracy more than 96% to MIR MNIST data set encoded by the device is achieved using a trained spiking neural network (SNN).

Suggested Citation

  • Fakun Wang & Fangchen Hu & Mingjin Dai & Song Zhu & Fangyuan Sun & Ruihuan Duan & Chongwu Wang & Jiayue Han & Wenjie Deng & Wenduo Chen & Ming Ye & Song Han & Bo Qiang & Yuhao Jin & Yunda Chua & Nan C, 2023. "A two-dimensional mid-infrared optoelectronic retina enabling simultaneous perception and encoding," Nature Communications, Nature, vol. 14(1), pages 1-8, December.
  • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-37623-5
    DOI: 10.1038/s41467-023-37623-5
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    References listed on IDEAS

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    1. Akhil Dodda & Nicholas Trainor & Joan. M. Redwing & Saptarshi Das, 2022. "All-in-one, bio-inspired, and low-power crypto engines for near-sensor security based on two-dimensional memtransistors," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    2. Shiva Subbulakshmi Radhakrishnan & Amritanand Sebastian & Aaryan Oberoi & Sarbashis Das & Saptarshi Das, 2021. "A biomimetic neural encoder for spiking neural network," Nature Communications, Nature, vol. 12(1), pages 1-10, December.
    3. Hongwei Tan & Quanzheng Tao & Ishan Pande & Sayani Majumdar & Fu Liu & Yifan Zhou & Per O. Å. Persson & Johanna Rosen & Sebastiaan van Dijken, 2020. "Tactile sensory coding and learning with bio-inspired optoelectronic spiking afferent nerves," Nature Communications, Nature, vol. 11(1), pages 1-9, December.
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    1. Zhuiri Peng & Lei Tong & Wenhao Shi & Langlang Xu & Xinyu Huang & Zheng Li & Xiangxiang Yu & Xiaohan Meng & Xiao He & Shengjie Lv & Gaochen Yang & Hao Hao & Tian Jiang & Xiangshui Miao & Lei Ye, 2024. "Multifunctional human visual pathway-replicated hardware based on 2D materials," Nature Communications, Nature, vol. 15(1), pages 1-9, December.

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