Neuromorphic spintronics

被引:591
|
作者
Grollier, J. [1 ]
Querlioz, D. [2 ]
Camsari, K. Y. [3 ]
Everschor-Sitte, K. [4 ]
Fukami, S. [5 ]
Stiles, M. D. [6 ]
机构
[1] Univ Paris Saclay, Unite Mixte Phys CNRS, Thales, Palaiseau, France
[2] Univ Paris Saclay, CNRS, Ctr Nanosci & Nanotechnol, Palaiseau, France
[3] Purdue Univ, Sch Elect & Comp Engn, W Lafayette, IN 47907 USA
[4] Johannes Gutenberg Univ Mainz, Inst Phys, Mainz, Germany
[5] Tohoku Univ, Elect Commun Res Inst, Sendai, Miyagi, Japan
[6] NIST, Gaithersburg, MD 20899 USA
基金
欧洲研究理事会;
关键词
SPIN-ORBIT TORQUE; MAGNETIC SKYRMIONS; PHASE-LOCKING; DRIVEN; DYNAMICS; MEMORY; SYNCHRONIZATION; OSCILLATIONS; ALGORITHM; MEMRISTOR;
D O I
10.1038/s41928-019-0360-9
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
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. 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.
引用
收藏
页码:360 / 370
页数:11
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