| Autor: |
Sun, Yiming, Lin, Tao, Lei, Na, Chen, Xing, Kang, Wang, Zhao, Zhiyuan, Wei, Dahai, Chen, Chao, Pang, Simin, Hu, Linglong, Yang, Liu, Dong, Enxuan, Zhao, Li, Liu, Lei, Yuan, Zhe, Ullrich, Aladin, Back, Christian H., Zhang, Jun, Pan, Dong, Zhao, Jianhua |
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| Zdroj: |
Nature Communications; 6/10/2023, Vol. 14 Issue 1, p1-10, 10p |
| Abstrakt: |
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. An energy-efficient physical reservoir is crucial for reservoir computing (RC). Here the authors demonstrate an all-electric skyrmion-enhanced strain-mediated physical RC system and achieve a benchmark chaotic time series prediction. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
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