Nature Communications (Jun 2023)

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,
  • Weisheng Zhao

DOI
https://doi.org/10.1038/s41467-023-39207-9
Journal volume & issue
Vol. 14, no. 1
pp. 1 – 10

Abstract

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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.