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Interface-type tunable oxygen ion dynamics for physical reservoir computing

Author

Listed:
  • Zhuohui Liu

    (Chinese Academy of Sciences
    University of Chinese Academy of Sciences)

  • Qinghua Zhang

    (Chinese Academy of Sciences
    Yangtze River Delta Physics Research Center Co. Ltd.)

  • Donggang Xie

    (Chinese Academy of Sciences
    University of Chinese Academy of Science)

  • Mingzhen Zhang

    (Chinese Academy of Sciences
    University of Chinese Academy of Science)

  • Xinyan Li

    (Chinese Academy of Sciences
    University of Chinese Academy of Sciences)

  • Hai Zhong

    (Chinese Academy of Sciences
    Ludong University)

  • Ge Li

    (Chinese Academy of Sciences
    University of Chinese Academy of Science)

  • Meng He

    (Chinese Academy of Sciences)

  • Dashan Shang

    (Chinese Academy of Sciences)

  • Can Wang

    (Chinese Academy of Sciences
    University of Chinese Academy of Science)

  • Lin Gu

    (Tsinghua University)

  • Guozhen Yang

    (Chinese Academy of Sciences)

  • Kuijuan Jin

    (Chinese Academy of Sciences
    University of Chinese Academy of Science)

  • Chen Ge

    (Chinese Academy of Sciences
    University of Chinese Academy of Science)

Abstract

Reservoir computing can more efficiently be used to solve time-dependent tasks than conventional feedforward network owing to various advantages, such as easy training and low hardware overhead. Physical reservoirs that contain intrinsic nonlinear dynamic processes could serve as next-generation dynamic computing systems. High-efficiency reservoir systems require nonlinear and dynamic responses to distinguish time-series input data. Herein, an interface-type dynamic transistor gated by an Hf0.5Zr0.5O2 (HZO) film was introduced to perform reservoir computing. The channel conductance of Mott material La0.67Sr0.33MnO3 (LSMO) can effectively be modulated by taking advantage of the unique coupled property of the polarization process and oxygen migration in hafnium-based ferroelectrics. The large positive value of the oxygen vacancy formation energy and negative value of the oxygen affinity energy resulted in the spontaneous migration of accumulated oxygen ions in the HZO films to the channel, leading to the dynamic relaxation process. The modulation of the channel conductance was found to be closely related to the current state, identified as the origin of the nonlinear response. In the time series recognition and prediction tasks, the proposed reservoir system showed an extremely low decision-making error. This work provides a promising pathway for exploiting dynamic ion systems for high-performance neural network devices.

Suggested Citation

  • Zhuohui Liu & Qinghua Zhang & Donggang Xie & Mingzhen Zhang & Xinyan Li & Hai Zhong & Ge Li & Meng He & Dashan Shang & Can Wang & Lin Gu & Guozhen Yang & Kuijuan Jin & Chen Ge, 2023. "Interface-type tunable oxygen ion dynamics for physical reservoir computing," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
  • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-42993-x
    DOI: 10.1038/s41467-023-42993-x
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    References listed on IDEAS

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    1. Chao Du & Fuxi Cai & Mohammed A. Zidan & Wen Ma & Seung Hwan Lee & Wei D. Lu, 2017. "Reservoir computing using dynamic memristors for temporal information processing," Nature Communications, Nature, vol. 8(1), pages 1-10, December.
    2. Ge Li & Donggang Xie & Hai Zhong & Ziye Zhang & Xingke Fu & Qingli Zhou & Qiang Li & Hao Ni & Jiaou Wang & Er-jia Guo & Meng He & Can Wang & Guozhen Yang & Kuijuan Jin & Chen Ge, 2022. "Photo-induced non-volatile VO2 phase transition for neuromorphic ultraviolet sensors," Nature Communications, Nature, vol. 13(1), pages 1-9, December.
    3. Rui Guo & Lu You & Weinan Lin & Amr Abdelsamie & Xinyu Shu & Guowei Zhou & Shaohai Chen & Liang Liu & Xiaobing Yan & Junling Wang & Jingsheng Chen, 2020. "Continuously controllable photoconductance in freestanding BiFeO3 by the macroscopic flexoelectric effect," Nature Communications, Nature, vol. 11(1), pages 1-9, December.
    4. Yanan Zhong & Jianshi Tang & Xinyi Li & Bin Gao & He Qian & Huaqiang Wu, 2021. "Dynamic memristor-based reservoir computing for high-efficiency temporal signal processing," Nature Communications, Nature, vol. 12(1), pages 1-9, December.
    5. Jacob Torrejon & Mathieu Riou & Flavio Abreu Araujo & Sumito Tsunegi & Guru Khalsa & Damien Querlioz & Paolo Bortolotti & Vincent Cros & Kay Yakushiji & Akio Fukushima & Hitoshi Kubota & Shinji Yuasa , 2017. "Neuromorphic computing with nanoscale spintronic oscillators," Nature, Nature, vol. 547(7664), pages 428-431, July.
    6. Kristof Vandoorne & Pauline Mechet & Thomas Van Vaerenbergh & Martin Fiers & Geert Morthier & David Verstraeten & Benjamin Schrauwen & Joni Dambre & Peter Bienstman, 2014. "Experimental demonstration of reservoir computing on a silicon photonics chip," Nature Communications, Nature, vol. 5(1), pages 1-6, May.
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