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Skyrmion-based artificial synapses for neuromorphic computing

Abstract

Magnetic skyrmions are topologically protected spin textures that have nanoscale dimensions and can be manipulated by an electric current. These properties make the structures potential information carriers in data storage, processing and transmission devices. However, the development of functional all-electrical electronic devices based on skyrmions remains challenging. Here we show that the current-induced creation, motion, detection and deletion of skyrmions at room temperature can be used to mimic the potentiation and depression behaviours of biological synapses. In particular, the accumulation and dissipation of magnetic skyrmions in ferrimagnetic multilayers can be controlled with electrical pulses to represent the variations in the synaptic weights. Using chip-level simulations, we demonstrate that such artificial synapses based on magnetic skyrmions could be used for neuromorphic computing tasks such as pattern recognition. For a handwritten pattern dataset, our system achieves a recognition accuracy of ~89%, which is comparable to the accuracy achieved with software-based ideal training (~93%).

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Fig. 1: Schematic of the experimental set-up and X-ray imaging of domain structures.
Fig. 2: Magnetic skyrmion-based artificial synapses.
Fig. 3: The pattern recognition simulation using the skyrmion synapse.
Fig. 4: Circuit implementation simulation using skyrmion synapse arrays.

Data availability

The data that support the plots within this paper and other findings of this study are available from the corresponding authors upon reasonable request.

Code availability

The micromagnetic simulator OOMMF used in this work is publicly accessible at http://math.nist.gov/oommf.

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Acknowledgements

This work was mainly supported by a KIST Institutional Program (2E29410). S.W. acknowledges support from IBM Research and management support from G. Hu and D. Worledge. S.W. also acknowledges K.-Y. Lee for providing the artwork included in Fig. 2. K.M.S., S.C., T.-E.P. and J.C. acknowledge support from the National Research Council of Science and Technology (NST; grant no. CAP-16-01-KIST) by the Korean government (MSIP). K.K. acknowledges support from the Basic Research Laboratory Program through the National Research Foundation of Korea (NRF) funded by MSIT (NRF-2018R1A4A1020696). J.-S.J. and H.J. acknowledge support from the Korea National Research Foundation programme (NRF-2017R1E1A1A01077484), which was particularly utilized to conduct the MNIST pattern work of this research. J.C. acknowledges support from the Yonsei-KIST Convergence Research Institute. The PolLux endstation was financed by the German Bundesministerium für Bildung und Forschung under grant no. 05K16WED and 05K19WE2. X.Z. was supported by the Guangdong Basic and Applied Basic Research Fund (grant no. 19201910240003361), and the Presidential Postdoctoral Fellowship of The Chinese University of Hong Kong, Shenzhen (CUHKSZ). Y.Z. acknowledges support by the President’s Fund of CUHKSZ, Longgang Key Laboratory of Applied Spintronics, National Natural Science Foundation of China (grant nos. 11974298 and 61961136006), Shenzhen Fundamental Research Fund (grant no. JCYJ20170410171958839) and Shenzhen Peacock Group Plan (grant no. KQTD20180413181702403). W.Z. and W.K. acknowledge support by the National Natural Science Foundation of China (grant no. 61627813), the International Collaboration Project B16001 and the National Key Technology Program of China (2017ZX01032101). Parts of this work were performed at the PolLux (X07DA) endstation of the Swiss Light Source, Paul Scherrer Institut, Switzerland.

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Contributions

S.W. designed, planned and initiated the study. K.M.S. grew films, fabricated devices and performed initial device characterizations. S.C. provided device fabrication support using electron-beam lithography. S.W., K.M.S., T.-E.P., K.K., S.F. and J.R. performed STXM experiments at Swiss Light Source. J.-S.J. and H.J. performed the neuromorphic computing simulation work. X.Z., J.X. and Y.Z. performed simulation on the ideal skyrmion synapse devices, and B.P., W.Z. and W.K. performed circuit implementation simulations. K.M.S., H.J. and S.W. drafted the manuscript and all authors reviewed it.

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Correspondence to Seonghoon Woo.

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Supplementary Figs. 1–8, Tables 1 and 2 and refs. 1–4.

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Song, K.M., Jeong, JS., Pan, B. et al. Skyrmion-based artificial synapses for neuromorphic computing. Nat Electron 3, 148–155 (2020). https://doi.org/10.1038/s41928-020-0385-0

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