油藏计算
人工神经网络
尖峰神经网络
突触
物理
生物神经网络
作者
Kyung Mee Song,Jaeseung Jeong,Sun Kyung Cha,Tae Eon Park,Kwangsu Kim,Simone Finizio,Jörg Raabe,Joonyeon Chang,Hyunsu Ju,Seonghoon Woo
出处
期刊:arXiv: Applied Physics
日期:2019-07-01
被引量:115
标识
DOI:10.1038/s41928-020-0385-0
摘要
Since the experimental discovery of magnetic skyrmions achieved one decade ago, there have been significant efforts to bring the virtual particles into all-electrical fully functional devices, inspired by their fascinating physical and topological properties suitable for future low-power electronics. Here, we experimentally demonstrate such a device: electrically-operating skyrmion-based artificial synaptic device designed for neuromorphic computing. We present that controlled current-induced creation, motion, detection and deletion of skyrmions in ferrimagnetic multilayers can be harnessed in a single device at room temperature to imitate the behaviors of biological synapses. Using simulations, we demonstrate that such skyrmion-based synapses could be used to perform neuromorphic pattern-recognition computing using handwritten recognition data set, reaching to the accuracy of ~89 percents, comparable to the software-based training accuracy of ~94 percents. Chip-level simulation then highlights the potential of skyrmion synapse compared to existing technologies. Our findings experimentally illustrate the basic concepts of skyrmion-based fully functional electronic devices while providing a new building block in the emerging field of spintronics-based bio-inspired computing.
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