• Media type: E-Article
  • Title: Experimental demonstration of a skyrmion-enhanced strain-mediated physical reservoir computing system
  • Contributor: Sun, Yiming [Author]; Lin, Tao [Author]; Lei, Na [Author]; Chen, Xing [Author]; Kang, Wang [Author]; Zhao, Zhiyuan [Author]; Wei, Dahai [Author]; Chen, Chao [Author]; Pang, Simin [Author]; Hu, Linglong [Author]; Yang, Liu [Author]; Dong, Enxuan [Author]; Zhao, Li [Author]; Liu, Lei [Author]; Yuan, Zhe [Author]; Ullrich, Aladin [Author]; Back, Christian H. [Author]; Zhang, Jun [Author]; Pan, Dong [Author]; Zhao, Jianhua [Author]; Feng, Ming [Author]; Fert, Albert [Author]; Zhao, Weisheng [Author]
  • imprint: Augsburg University Publication Server (OPUS), 2023
  • Language: English
  • DOI: https://doi.org/10.1038/s41467-023-39207-9
  • Origination:
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  • Description: 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.
  • Access State: Open Access