Spin–Orbit Torque‐Induced Domain Nucleation for Neuromorphic Computing
Jing Zhou, Tieyang Zhao, Xinyu Shu, Liang Liu, Weinan Lin, Shaohai Chen, Shu Shi, Xiaobing Yan, Xiaogang Liu, Jingsheng Chen
Abstract
Abstract Neuromorphic computing has become an increasingly popular approach for artificial intelligence because it can perform cognitive tasks more efficiently than conventional computers. However, it remains challenging to develop dedicated hardware for artificial neural networks. Here, a simple bilayer spintronic device for hardware implementation of neuromorphic computing is demonstrated. In L1 1 ‐CuPt/CoPt bilayer, current‐inducted field‐free magnetization switching by symmetry‐dependent spin–orbit torques shows a unique domain nucleation‐dominated magnetization reversal, which is not accessible in conventional bilayers. Gradual domain nucleation creates multiple intermediate magnetization states which form the basis of a sigmoidal neuron. Using the L1 1 ‐CuPt/CoPt bilayer as a sigmoidal neuron, the training of a deep learning network to recognize written digits, with a high recognition rate (87.5%) comparable to simulation (87.8%) is further demonstrated. This work offers a new scheme of implementing artificial neural networks by magnetic domain nucleation.