神经形态工程学
双层
材料科学
人工神经网络
成核
自旋电子学
凝聚态物理
领域(数学分析)
磁化动力学
人工智能
领域(数学)
记忆电阻器
物理
计算机科学
磁化
纳米技术
磁场
电子工程
工程类
数学
量子力学
铁磁性
化学
膜
数学分析
生物化学
热力学
纯数学
作者
Jing Zhou,Tieyang Zhao,Xinyu Shu,Liang Liu,Weinan Lin,Shaohai Chen,Shu Shi,Xiaobing Yan,Xiaogang Liu,Jingsheng Chen
标识
DOI:10.1002/adma.202103672
摘要
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 L11 -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 L11 -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.
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