Interface-type tunable oxygen ion dynamics for physical reservoir computing

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 () and Chen Ge ()
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Zhuohui Liu: Chinese Academy of Sciences
Qinghua Zhang: Chinese Academy of Sciences
Donggang Xie: Chinese Academy of Sciences
Mingzhen Zhang: Chinese Academy of Sciences
Xinyan Li: Chinese Academy of Sciences
Hai Zhong: Chinese Academy of Sciences
Ge Li: Chinese Academy of Sciences
Meng He: Chinese Academy of Sciences
Dashan Shang: Chinese Academy of Sciences
Can Wang: Chinese Academy of Sciences
Lin Gu: Tsinghua University
Guozhen Yang: Chinese Academy of Sciences
Kuijuan Jin: Chinese Academy of Sciences
Chen Ge: Chinese Academy of Sciences

Nature Communications, 2023, vol. 14, issue 1, 1-11

Abstract: 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.

Date: 2023
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DOI: 10.1038/s41467-023-42993-x

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