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Graphene/silicon heterojunction for reconfigurable phase-relevant activation function in coherent optical neural networks

Author

Listed:
  • Chuyu Zhong

    (Zhejiang University)

  • Kun Liao

    (Peking University)

  • Tianxiang Dai

    (Peking University)

  • Maoliang Wei

    (Zhejiang University)

  • Hui Ma

    (Zhejiang University)

  • Jianghong Wu

    (Westlake University
    Westlake Institute for Advanced Study)

  • Zhibin Zhang

    (Peking University)

  • Yuting Ye

    (Westlake University
    Westlake Institute for Advanced Study)

  • Ye Luo

    (Westlake University
    Westlake Institute for Advanced Study)

  • Zequn Chen

    (Westlake University
    Westlake Institute for Advanced Study)

  • Jialing Jian

    (Westlake University
    Westlake Institute for Advanced Study)

  • Chunlei Sun

    (Westlake University
    Westlake Institute for Advanced Study)

  • Bo Tang

    (Institute of Microelectronics of the Chinese Academy of Sciences)

  • Peng Zhang

    (Institute of Microelectronics of the Chinese Academy of Sciences)

  • Ruonan Liu

    (Institute of Microelectronics of the Chinese Academy of Sciences)

  • Junying Li

    (Zhejiang University)

  • Jianyi Yang

    (Zhejiang University)

  • Lan Li

    (Westlake University
    Westlake Institute for Advanced Study)

  • Kaihui Liu

    (Peking University)

  • Xiaoyong Hu

    (Peking University)

  • Hongtao Lin

    (Zhejiang University
    Zhejiang University)

Abstract

Optical neural networks (ONNs) herald a new era in information and communication technologies and have implemented various intelligent applications. In an ONN, the activation function (AF) is a crucial component determining the network performances and on-chip AF devices are still in development. Here, we first demonstrate on-chip reconfigurable AF devices with phase activation fulfilled by dual-functional graphene/silicon (Gra/Si) heterojunctions. With optical modulation and detection in one device, time delays are shorter, energy consumption is lower, reconfigurability is higher and the device footprint is smaller than other on-chip AF strategies. The experimental modulation voltage (power) of our Gra/Si heterojunction achieves as low as 1 V (0.5 mW), superior to many pure silicon counterparts. In the photodetection aspect, a high responsivity of over 200 mA/W is realized. Special nonlinear functions generated are fed into a complex-valued ONN to challenge handwritten letters and image recognition tasks, showing improved accuracy and potential of high-efficient, all-component-integration on-chip ONN. Our results offer new insights for on-chip ONN devices and pave the way to high-performance integrated optoelectronic computing circuits.

Suggested Citation

  • Chuyu Zhong & Kun Liao & Tianxiang Dai & Maoliang Wei & Hui Ma & Jianghong Wu & Zhibin Zhang & Yuting Ye & Ye Luo & Zequn Chen & Jialing Jian & Chunlei Sun & Bo Tang & Peng Zhang & Ruonan Liu & Junyin, 2023. "Graphene/silicon heterojunction for reconfigurable phase-relevant activation function in coherent optical neural networks," Nature Communications, Nature, vol. 14(1), pages 1-9, December.
  • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-42116-6
    DOI: 10.1038/s41467-023-42116-6
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    References listed on IDEAS

    as
    1. J. Feldmann & N. Youngblood & C. D. Wright & H. Bhaskaran & W. H. P. Pernice, 2019. "All-optical spiking neurosynaptic networks with self-learning capabilities," Nature, Nature, vol. 569(7755), pages 208-214, May.
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