Ferroic tunnel junctions and their application in neuromorphic networks

被引:110
作者
Guo, Rui [1 ,2 ]
Lin, Weinan [1 ]
Yan, Xiaobing [1 ,2 ]
Venkatesan, T. [1 ,3 ,4 ,5 ,6 ]
Chen, Jingsheng [1 ]
机构
[1] Natl Univ Singapore, Dept Mat Sci & Engn, Singapore 117575, Singapore
[2] Hebei Univ, Coll Electron & Informat Engn, Baoding 071002, Peoples R China
[3] Natl Univ Singapore, NUSNNI Nanocore, Singapore 117411, Singapore
[4] Natl Univ Singapore, Dept Elect & Comp Sci Engn, Singapore 117583, Singapore
[5] Natl Univ Singapore, Dept Phys, Singapore 117542, Singapore
[6] Natl Univ Singapore, Integrat Sci & Engn, Singapore 119077, Singapore
来源
APPLIED PHYSICS REVIEWS | 2020年 / 7卷 / 01期
基金
新加坡国家研究基金会; 中国国家自然科学基金;
关键词
RANDOM-ACCESS MEMORY; SPIN-ORBIT TORQUE; PHASE-CHANGE; ROOM-TEMPERATURE; DOMAIN-WALLS; LARGE MAGNETORESISTANCE; FERROELECTRIC CONTROL; FORMING-FREE; ELECTRORESISTANCE; INTERFACE;
D O I
10.1063/1.5120565
中图分类号
O59 [应用物理学];
学科分类号
摘要
Brain-inspired neuromorphic computing has been intensively studied due to its potential to address the inherent energy and throughput limitations of conventional Von-Neumann based computing architecture. Memristors are ideal building blocks for artificial synapses, which are the fundamental components of neuromorphic computing. In recent years, the emerging ferroic (ferroelectric and ferromagnetic) tunnel junctions have been shown to be able to function as memristors, which are potential candidates to emulate artificial synapses for neuromorphic computing. Here, we provide a review on the ferroic tunnel junctions and their applications as artificial synapses in neuromorphic networks. We focus on the development history of ferroic tunnel junctions, their physical conduction mechanisms, and the intrinsic dynamics of memristors. Their current applications in neuromorphic networks will also be discussed. Finally, a conclusion and future outlooks on the development of ferroic tunnel junctions will be given. Our goal is to give a broad review of ferroic tunnel junction based artificial synapses that can be applied to neuromorphic computing and to help further ongoing research in this field.
引用
收藏
页数:25
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