神经形态工程学
自旋电子学
隧道磁电阻
电子线路
计算机体系结构
计算机科学
材料科学
电子工程
人工神经网络
纳米技术
电气工程
人工智能
物理
工程类
图层(电子)
铁磁性
量子力学
作者
Julie Grollier,Damien Querlioz,Kerem Y. Çamsarı,Karin Everschor‐Sitte,Shunsuke Fukami,M. D. Stiles
标识
DOI:10.1038/s41928-019-0360-9
摘要
Neuromorphic computing uses brain-inspired principles to design circuits that can perform computational tasks with superior power efficiency to conventional computers. Approaches that use traditional electronic devices to create artificial neurons and synapses are, however, currently limited by the energy and area requirements of these components. Spintronic nanodevices, which exploit both the magnetic and electrical properties of electrons, can increase the energy efficiency and decrease the area of these circuits, and magnetic tunnel junctions are of particular interest as neuromorphic computing elements because they are compatible with standard integrated circuits and can support multiple functionalities. Here, we review the development of spintronic devices for neuromorphic computing. We examine how magnetic tunnel junctions can serve as synapses and neurons, and how magnetic textures, such as domain walls and skyrmions, can function as neurons. We also explore spintronics-based implementations of neuromorphic computing tasks, such as pattern recognition in an associative memory, and discuss the challenges that exist in scaling up these systems. This Review Article examines the development of spintronic devices for neuromorphic computing, exploring how magnetic tunnel junctions and magnetic textures can act as artificial neurons and synapses, as well as considering the challenges that exist in scaling up current systems.
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