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All-ferroelectric implementation of reservoir computing

Author

Listed:
  • 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
    Nanjing University)

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.

Suggested Citation

  • Zhiwei Chen & Wenjie Li & Zhen Fan & Shuai Dong & Yihong Chen & Minghui Qin & Min Zeng & Xubing Lu & Guofu Zhou & Xingsen Gao & Jun-Ming Liu, 2023. "All-ferroelectric implementation of reservoir computing," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
  • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-39371-y
    DOI: 10.1038/s41467-023-39371-y
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