稳健性(进化)
隧道磁电阻
自旋电子学
磁化动力学
计算机科学
电压
可扩展性
垂直的
磁场
材料科学
尖峰神经网络
非线性系统
磁化
物理
光电子学
电气工程
纳米技术
凝聚态物理
人工神经网络
工程类
图层(电子)
化学
人工智能
铁磁性
基因
数据库
量子力学
生物化学
数学
几何学
作者
Louis Farcis,B. M. S. Teixeira,Philippe Talatchian,D. Salomoni,U. Ebels,S. Auffret,B. Diény,Frank Alice Mizrahi,Julie Grollier,R. C. Sousa,L. D. Buda-Prejbeanu
出处
期刊:Nano Letters
[American Chemical Society]
日期:2023-08-17
卷期号:23 (17): 7869-7875
被引量:10
标识
DOI:10.1021/acs.nanolett.3c01597
摘要
Spintronic devices have recently attracted a lot of attention in the field of unconventional computing due to their non-volatility for short- and long-term memory, nonlinear fast response, and relatively small footprint. Here we demonstrate experimentally how voltage driven magnetization dynamics of dual free layer perpendicular magnetic tunnel junctions can emulate spiking neurons in hardware. The output spiking rate was controlled by varying the dc bias voltage across the device. The field-free operation of this two-terminal device and its robustness against an externally applied magnetic field make it a suitable candidate to mimic the neuron response in a dense neural network. The small energy consumption of the device (4-16 pJ/spike) and its scalability are important benefits for embedded applications. This compact perpendicular magnetic tunnel junction structure could finally bring spiking neural networks to sub-100 nm size elements.
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