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Neuromorphic device based on silicon nanosheets

Author

Listed:
  • Chenhao Wang

    (Zhejiang University)

  • Xinyi Xu

    (ZJU-Hangzhou Global Scientific and Technological Innovation Centre
    Zhejiang University
    Zhejiang University
    Zhejiang University)

  • Xiaodong Pi

    (Zhejiang University
    ZJU-Hangzhou Global Scientific and Technological Innovation Centre)

  • Mark D. Butala

    (Zhejiang University)

  • Wen Huang

    (Nanjing University of Posts and Telecommunications)

  • Lei Yin

    (Zhejiang University)

  • Wenbing Peng

    (Zhejiang University)

  • Munir Ali

    (ZJU-Hangzhou Global Scientific and Technological Innovation Centre
    Zhejiang University)

  • Srikrishna Chanakya Bodepudi

    (ZJU-Hangzhou Global Scientific and Technological Innovation Centre
    Zhejiang University)

  • Xvsheng Qiao

    (Zhejiang University)

  • Yang Xu

    (ZJU-Hangzhou Global Scientific and Technological Innovation Centre
    Zhejiang University
    Zhejiang University
    Zhejiang University)

  • Wei Sun

    (Zhejiang University)

  • Deren Yang

    (Zhejiang University
    ZJU-Hangzhou Global Scientific and Technological Innovation Centre)

Abstract

Silicon is vital for its high abundance, vast production, and perfect compatibility with the well-established CMOS processing industry. Recently, artificially stacked layered 2D structures have gained tremendous attention via fine-tuning properties for electronic devices. This article presents neuromorphic devices based on silicon nanosheets that are chemically exfoliated and surface-modified, enabling self-assembly into hierarchical stacking structures. The device functionality can be switched between a unipolar memristor and a feasibly reset-able synaptic device. The memory function of the device is based on the charge storage in the partially oxidized SiNS stacks followed by the discharge activated by the electric field at the Au-Si Schottky interface, as verified in both experimental and theoretical means. This work further inspired elegant neuromorphic computation models for digit recognition and noise filtration. Ultimately, it brings silicon - the most established semiconductor - back to the forefront for next-generation computations.

Suggested Citation

  • Chenhao Wang & Xinyi Xu & Xiaodong Pi & Mark D. Butala & Wen Huang & Lei Yin & Wenbing Peng & Munir Ali & Srikrishna Chanakya Bodepudi & Xvsheng Qiao & Yang Xu & Wei Sun & Deren Yang, 2022. "Neuromorphic device based on silicon nanosheets," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-32884-y
    DOI: 10.1038/s41467-022-32884-y
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    References listed on IDEAS

    as
    1. Jing Pei & Lei Deng & Sen Song & Mingguo Zhao & Youhui Zhang & Shuang Wu & Guanrui Wang & Zhe Zou & Zhenzhi Wu & Wei He & Feng Chen & Ning Deng & Si Wu & Yu Wang & Yujie Wu & Zheyu Yang & Cheng Ma & G, 2019. "Towards artificial general intelligence with hybrid Tianjic chip architecture," Nature, Nature, vol. 572(7767), pages 106-111, August.
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    3. Myungsoo Kim & Ruijing Ge & Xiaohan Wu & Xing Lan & Jesse Tice & Jack C. Lee & Deji Akinwande, 2018. "Zero-static power radio-frequency switches based on MoS2 atomristors," Nature Communications, Nature, vol. 9(1), pages 1-7, December.
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    Cited by:

    1. Zhenjia Chen & Zhenyuan Lin & Ji Yang & Cong Chen & Di Liu & Liuting Shan & Yuanyuan Hu & Tailiang Guo & Huipeng Chen, 2024. "Cross-layer transmission realized by light-emitting memristor for constructing ultra-deep neural network with transfer learning ability," Nature Communications, Nature, vol. 15(1), pages 1-12, December.

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