Neuromorphic Computing in Synthetic Antiferromagnets by Spin‐Orbit Torque Induced Magnetic‐Field‐Free Magnetization Switching

Author:

Han Xiang1,Wang Zhenxing1,Wang Yiheng1,Wang Di23,Zheng Limei1,Zhao Le4,Huang Qikun5,Cao Qiang5,Chen Yanxue1ORCID,Bai Lihui1ORCID,Xing Guozhong23,Tian Yufeng1ORCID,Yan Shishen15ORCID

Affiliation:

1. School of Physics State Key Laboratory of Crystal Materials Shandong University Jinan 250100 China

2. Key Laboratory of Microelectronic Devices and Integrated Technology Institute of Microelectronics Chinese Academy of Sciences Beijing 100029 China

3. University of Chinese Academy of Sciences Beijing 100029 China

4. School of Information and Automation Engineering Qilu University of Technology Jinan 250353 China

5. Spintronics Institute University of Jinan Jinan 250022 China

Abstract

AbstractSynthetic antiferromagnet (SAF) with high thermal stability, ultra‐fast spin dynamics, and highly efficient spin‐orbit torque switching has great application potential in neuromorphic computing hardware. However, two challenges, the weakening of Hall signal in the remanent state and the need for a large auxiliary magnetic field for perpendicular magnetization switching, greatly limit the advantages of SAF in neuromorphic computing. In this work, both the enhanced anomalous Hall resistance and magnetic‐field‐free perpendicular magnetization switching are achieved by using oblique sputtering to fabricate the Pt/CoPt/Ru/CoTb SAF with strong interlayer exchange coupling and magnetic moment compensation. The fabricated SAF as synapse shows nearly linear, nonvolatile multistate plasticity, and as neuron exhibits a nonlinear sigmoid activation function, which are used to construct a fully connected neural network with a remarkable 97.0–98.1% recognition rate for the handwritten digits. Additionally, SAF serving as spike‐timing‐dependent plasticity synapse is used to construct an adaptive, unsupervised learning spiking neural network, and achieve an 87.0% accuracy in handwritten digit recognition. The findings exhibit the promise of SAFs as specialized hardware for high‐performance neuromorphic computing, offering high recognition rates and low power consumption.

Funder

National Natural Science Foundation of China

National Key Research and Development Program of China

Publisher

Wiley

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