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

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, Christian H. Back, Jun Zhang, Dong Pan, Jianhua Zhao, Ming Feng (), Albert Fert and Weisheng Zhao
Additional contact information
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
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
Jun Zhang: 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
Weisheng Zhao: Beihang University

Nature Communications, 2023, vol. 14, issue 1, 1-10

Abstract: 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.

Date: 2023
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DOI: 10.1038/s41467-023-39207-9

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