Distinguishing artificial spin ice states using magnetoresistance effect for neuromorphic computing

Wenjie Hu, Zefeng Zhang, Yanghui Liao, Qiang Li, Yang Shi, Huanyu Zhang, Xumeng Zhang, Chang Niu, Yu Wu, Weichao Yu, Xiaodong Zhou, Hangwen Guo, Wenbin Wang, Jiang Xiao, Lifeng Yin (), Qi Liu () and Jian Shen ()
Additional contact information
Wenjie Hu: Fudan University
Zefeng Zhang: Fudan University
Yanghui Liao: Fudan University
Qiang Li: Fudan University
Yang Shi: Fudan University
Huanyu Zhang: Fudan University
Xumeng Zhang: Fudan University
Chang Niu: Fudan University
Yu Wu: Fudan University
Weichao Yu: Fudan University
Xiaodong Zhou: Fudan University
Hangwen Guo: Fudan University
Wenbin Wang: Fudan University
Jiang Xiao: Fudan University
Lifeng Yin: Fudan University
Qi Liu: Fudan University
Jian Shen: Fudan University

Nature Communications, 2023, vol. 14, issue 1, 1-9

Abstract: Abstract Artificial spin ice (ASI) consisting patterned array of nano-magnets with frustrated dipolar interactions offers an excellent platform to study frustrated physics using direct imaging methods. Moreover, ASI often hosts a large number of nearly degenerated and non-volatile spin states that can be used for multi-bit data storage and neuromorphic computing. The realization of the device potential of ASI, however, critically relies on the capability of transport characterization of ASI, which has not been demonstrated so far. Using a tri-axial ASI system as the model system, we demonstrate that transport measurements can be used to distinguish the different spin states of the ASI system. Specifically, by fabricating a tri-layer structure consisting a permalloy base layer, a Cu spacer layer and the tri-axial ASI layer, we clearly resolve different spin states in the tri-axial ASI system using lateral transport measurements. We have further demonstrated that the tri-axial ASI system has all necessary required properties for reservoir computing, including rich spin configurations to store input signals, nonlinear response to input signals, and fading memory effect. The successful transport characterization of ASI opens up the prospect for novel device applications of ASI in multi-bit data storage and neuromorphic computing.

Date: 2023
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DOI: 10.1038/s41467-023-38286-y

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