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Magnetic skyrmion artificial synapse for neuromorphic computing

Authors :
Song, Kyung Mee
Jeong, Jae-Seung
Pan, Biao
Zhang, Xichao
Xia, Jing
Cha, Sun Kyung
Park, Tae-Eon
Kim, Kwangsu
Finizio, Simone
Raabe, Joerg
Chang, Joonyeon
Zhou, Yan
Zhao, Weisheng
Kang, Wang
Ju, Hyunsu
Woo, Seonghoon
Source :
Nature Electronics 3, 148 (2020)
Publication Year :
2019

Abstract

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.<br />Comment: 11 pages, 4 figures

Details

Database :
arXiv
Journal :
Nature Electronics 3, 148 (2020)
Publication Type :
Report
Accession number :
edsarx.1907.00957
Document Type :
Working Paper
Full Text :
https://doi.org/10.1038/s41928-020-0385-0