Decision making module based on stochastic magnetic tunnel junctions

作     者：Yifan Miao Li Zhao Yajun Zhang Zhe Yuan 

作者机构：Center for Advanced Quantum Studies and Department of Physics Beijing Normal University Institute for Nanoelectric Devices and Quantum Computing Fudan University Interdisciplinary Center for Theoretical Physics and Information Sciences Fudan University 

出 版 物：《Science China(Physics,Mechanics & Astronomy)》 (中国科学:物理学 力学 天文学(英文版))

年 卷 期：2025年第Mechanics & Astronomy) .卷第1期

页      面：202-209页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 07[理学] 081104[工学-模式识别与智能系统] 070205[理学-凝聚态物理] 08[工学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 0702[理学-物理学] 

基　　金：supported by the National Natural Science Foundation of China (Grant No. 12174028) 

主　　题：neuromorphic computing spintronics magnetic tunnel junction neural networks 

摘      要：In biological neural systems, noise is ubiquitous but does not affect the correct decisions made in the complex cognitive ***-making in biological neural system is typically achieved by accumulating input information over a period of *** by recent developments in neurosciences, we design a decision-making module based on spintronic devices, utilizing superparamagnetic tunnel junctions as artificial neurons. The feasibility of this decision-making module is verified through circuit simulations. Taking a multi-layer perceptron as an example, the module significantly improves the accuracy of the perceptron in the handwritten digit recognition task. Furthermore, the spintronic decision-making module offers advantages over the conventional pooling methods, such as adaptive decision time, high performance and the absence of analog-to-digital conversion. The decision-making module is flexible to be integrated into artificial neural networks and provides a general yet effective solution to enhance performance against device noise.