All-ferroelectric implementation of reservoir computing

Zhiwei Chen, Wenjie Li, Zhen Fan (), Shuai Dong, Yihong Chen, Minghui Qin, Min Zeng, Xubing Lu, Guofu Zhou, Xingsen Gao and Jun-Ming Liu
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Zhiwei Chen: South China Normal University
Wenjie Li: South China Normal University
Zhen Fan: South China Normal University
Shuai Dong: South China Normal University
Yihong Chen: South China Normal University
Minghui Qin: South China Normal University
Min Zeng: South China Normal University
Xubing Lu: South China Normal University
Guofu Zhou: South China Normal University
Xingsen Gao: South China Normal University
Jun-Ming Liu: South China Normal University

Nature Communications, 2023, vol. 14, issue 1, 1-12

Abstract: Abstract Reservoir computing (RC) offers efficient temporal information processing with low training cost. All-ferroelectric implementation of RC is appealing because it can fully exploit the merits of ferroelectric memristors (e.g., good controllability); however, this has been undemonstrated due to the challenge of developing ferroelectric memristors with distinctly different switching characteristics specific to the reservoir and readout network. Here, we experimentally demonstrate an all-ferroelectric RC system whose reservoir and readout network are implemented with volatile and nonvolatile ferroelectric diodes (FDs), respectively. The volatile and nonvolatile FDs are derived from the same Pt/BiFeO3/SrRuO3 structure via the manipulation of an imprint field (Eimp). It is shown that the volatile FD with Eimp exhibits short-term memory and nonlinearity while the nonvolatile FD with negligible Eimp displays long-term potentiation/depression, fulfilling the functional requirements of the reservoir and readout network, respectively. Hence, the all-ferroelectric RC system is competent for handling various temporal tasks. In particular, it achieves an ultralow normalized root mean square error of 0.017 in the Hénon map time-series prediction. Besides, both the volatile and nonvolatile FDs demonstrate long-term stability in ambient air, high endurance, and low power consumption, promising the all-ferroelectric RC system as a reliable and low-power neuromorphic hardware for temporal information processing.

Date: 2023
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DOI: 10.1038/s41467-023-39371-y

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