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Experimental demonstration of a skyrmion-enhanced strain-mediated physical reservoir computing system

Author

Listed:
  • Yiming Sun

    (Beihang University)

  • Tao Lin

    (Beihang University)

  • Na Lei

    (Beihang University)

  • Xing Chen

    (Beihang University)

  • Wang Kang

    (Beihang University)

  • Zhiyuan Zhao

    (Chinese Academy of Sciences)

  • Dahai Wei

    (Chinese Academy of Sciences)

  • Chao Chen

    (Beihang University)

  • Simin Pang

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

  • Linglong Hu

    (Jilin Normal University)

  • Liu Yang

    (Beihang University)

  • Enxuan Dong

    (Beihang University)

  • Li Zhao

    (Beijing Normal University)

  • Lei Liu

    (Chinese Academy of Sciences)

  • Zhe Yuan

    (Beijing Normal University)

  • Aladin Ullrich

    (University of Augsburg)

  • Christian H. Back

    (Technical University of Munich
    Munich Center for Quantum Science and Technology (MCQST)
    Technical University of Munich)

  • Jun Zhang

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

  • Dong Pan

    (Chinese Academy of Sciences)

  • Jianhua Zhao

    (Chinese Academy of Sciences)

  • Ming Feng

    (Jilin Normal University)

  • Albert Fert

    (Beihang University
    Université Paris-Saclay)

  • Weisheng Zhao

    (Beihang University)

Abstract

Physical reservoirs holding intrinsic nonlinearity, high dimensionality, and memory effects have attracted considerable interest regarding solving complex tasks efficiently. Particularly, spintronic and strain-mediated electronic physical reservoirs are appealing due to their high speed, multi-parameter fusion and low power consumption. Here, we experimentally realize a skyrmion-enhanced strain-mediated physical reservoir in a multiferroic heterostructure of Pt/Co/Gd multilayers on (001)-oriented 0.7PbMg1/3Nb2/3O3−0.3PbTiO3 (PMN-PT). The enhancement is coming from the fusion of magnetic skyrmions and electro resistivity tuned by strain simultaneously. The functionality of the strain-mediated RC system is successfully achieved via a sequential waveform classification task with the recognition rate of 99.3% for the last waveform, and a Mackey-Glass time series prediction task with normalized root mean square error (NRMSE) of 0.2 for a 20-step prediction. Our work lays the foundations for low-power neuromorphic computing systems with magneto-electro-ferroelastic tunability, representing a further step towards developing future strain-mediated spintronic applications.

Suggested Citation

  • Yiming Sun & Tao Lin & Na Lei & Xing Chen & Wang Kang & Zhiyuan Zhao & Dahai Wei & Chao Chen & Simin Pang & Linglong Hu & Liu Yang & Enxuan Dong & Li Zhao & Lei Liu & Zhe Yuan & Aladin Ullrich & Chris, 2023. "Experimental demonstration of a skyrmion-enhanced strain-mediated physical reservoir computing system," Nature Communications, Nature, vol. 14(1), pages 1-10, December.
    • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-39207-9
    DOI: 10.1038/s41467-023-39207-9
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    References listed on IDEAS

    as
    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. 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.
    3. Xing Chen & Flavio Abreu Araujo & Mathieu Riou & Jacob Torrejon & Dafiné Ravelosona & Wang Kang & Weisheng Zhao & Julie Grollier & Damien Querlioz, 2022. "Forecasting the outcome of spintronic experiments with Neural Ordinary Differential Equations," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    4. 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.
    5. Na Lei & Thibaut Devolder & Guillaume Agnus & Pascal Aubert & Laurent Daniel & Joo-Von Kim & Weisheng Zhao & Theodossis Trypiniotis & Russell P. Cowburn & Claude Chappert & Dafiné Ravelosona & Philipp, 2013. "Strain-controlled magnetic domain wall propagation in hybrid piezoelectric/ferromagnetic structures," Nature Communications, Nature, vol. 4(1), pages 1-7, June.
    6. X. Z. Yu & Y. Onose & N. Kanazawa & J. H. Park & J. H. Han & Y. Matsui & N. Nagaosa & Y. Tokura, 2010. "Real-space observation of a two-dimensional skyrmion crystal," Nature, Nature, vol. 465(7300), pages 901-904, June.
    7. Klaus Raab & Maarten A. Brems & Grischa Beneke & Takaaki Dohi & Jan Rothörl & Fabian Kammerbauer & Johan H. Mentink & Mathias Kläui, 2022. "Brownian reservoir computing realized using geometrically confined skyrmion dynamics," Nature Communications, Nature, vol. 13(1), pages 1-6, December.
    8. You Ba & Shihao Zhuang & Yike Zhang & Yutong Wang & Yang Gao & Hengan Zhou & Mingfeng Chen & Weideng Sun & Quan Liu & Guozhi Chai & Jing Ma & Ying Zhang & Huanfang Tian & Haifeng Du & Wanjun Jiang & C, 2021. "Electric-field control of skyrmions in multiferroic heterostructure via magnetoelectric coupling," Nature Communications, Nature, vol. 12(1), pages 1-10, December.
    9. 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.
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