Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Skyrmion-based artificial synapses for neuromorphic computing

Nature Electronics volume 3, pages 148–155 (2020)Cite this article

Subjects

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%).

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

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.

Similar content being viewed by others

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.

References

  1. Fert, A., Reyren, N. & Cros, V. Magnetic skyrmions: advances in physics and potential applications. Nat. Rev. Mater. 2, 17031 (2017).

    Article  Google Scholar 

  2. Zhang, X. et al. Skyrmion-electronics: writing, deleting, reading and processing magnetic skyrmions toward spintronic applications. J. Phys. Condens. Matter 32, 143001 (2020).

    Article  Google Scholar 

  3. Mühlbauer, S. et al. Skyrmion lattice in a chiral magnet. Science 323, 915–919 (2009).

    Article  Google Scholar 

  4. Yu, X. Z. et al. Real-space observation of a two-dimensional skyrmion crystal. Nature 465, 901–904 (2010).

    Article  Google Scholar 

  5. Dzyaloshinsky, I. A thermodynamic theory of ‘weak’ ferromagnetism of antiferromagnetics. J. Phys. Chem. Solids 4, 241–255 (1958).

    Article  Google Scholar 

  6. Moriya, T. Anisotropic superexchange interaction and weak ferromagnetism. Phys. Rev. 120, 91–98 (1960).

    Article  Google Scholar 

  7. Jiang, W. et al. Blowing magnetic skyrmion bubbles. Science 349, 283–286 (2015).

    Article  Google Scholar 

  8. Woo, S. et al. Observation of room-temperature magnetic skyrmions and their current-driven dynamics in ultrathin metallic ferromagnets. Nat. Mater. 15, 501–506 (2016).

    Article  Google Scholar 

  9. Legrand, W. et al. Room-temperature current-induced generation and motion of sub-100 nm skyrmions. Nano Lett. 17, 2703–2712 (2017).

    Article  Google Scholar 

  10. Hrabec, A. et al. Current-induced skyrmion generation and dynamics in symmetric bilayers. Nat. Commun. 8, 15765 (2017).

    Article  Google Scholar 

  11. Büttner, F. et al. Field-free deterministic ultrafast creation of magnetic skyrmions by spin–orbit torques. Nat. Nanotechnol. 12, 1040–1044 (2017).

    Article  Google Scholar 

  12. Woo, S. et al. Deterministic creation and deletion of a single magnetic skyrmion observed by direct time-resolved X-ray microscopy. Nat. Electron. 1, 288–296 (2018).

    Google Scholar 

  13. Woo, S. et al. Current-driven dynamics and inhibition of the skyrmion Hall effect of ferrimagnetic skyrmions in GdFeCo films. Nat. Commun. 9, 959 (2018).

    Article  Google Scholar 

  14. Maccariello, D. et al. Electrical detection of single magnetic skyrmions in metallic multilayers at room temperature. Nat. Nanotechnol. 13, 233–237 (2018).

    Article  Google Scholar 

  15. Zeissler, K. et al. Discrete Hall resistivity contribution from Néel skyrmions in multilayer nanodiscs. Nat. Nanotechnol. 13, 1161–1166 (2018).

    Article  Google Scholar 

  16. Hopfield, J. J. Neural networks and physical systems with emergent collective computational abilities. Proc. Natl Acad. Sci. USA 79, 2554–2558 (1982).

    Article  MathSciNet  MATH  Google Scholar 

  17. Kuzum, D., Jeyasingh, R. G. D., Lee, B. & Wong, H.-S. P. Nanoelectronic programmable synapses based on phase change materials for brain-inspired computing. Nano Lett. 12, 2179–2186 (2012).

    Article  Google Scholar 

  18. Prezioso, M. et al. Training and operation of an integrated neuromorphic network based on metal-oxide memristors. Nature 521, 61–64 (2015).

    Article  Google Scholar 

  19. Lequeux, S. et al. A magnetic synapse: multilevel spin–torque memristor with perpendicular anisotropy. Sci. Rep. 6, 31510 (2016).

    Article  Google Scholar 

  20. Nagaosa, N. & Tokura, Y. Topological properties and dynamics of magnetic skyrmions. Nat. Nanotechnol. 8, 899–911 (2013).

    Article  Google Scholar 

  21. Huang, Y., Kang, W., Zhang, X., Zhou, Y. & Zhao, W. Magnetic skyrmion-based synaptic devices. Nanotechnology 28, 08LT02 (2017).

    Article  Google Scholar 

  22. Torrejon, J. et al. Neuromorphic computing with nanoscale spintronic oscillators. Nature 547, 428–431 (2017).

    Article  Google Scholar 

  23. Romera, M. et al. Vowel recognition with four coupled spin–torque nano-oscillators. Nature 563, 230–234 (2018).

    Article  Google Scholar 

  24. Bourianoff, G., Pinna, D., Sitte, M. & Everschor-Sitte, K. Potential implementation of reservoir computing models based on magnetic skyrmions. AIP Adv. 8, 055602 (2018).

    Article  Google Scholar 

  25. Zázvorka, J. et al. Thermal skyrmion diffusion used in a reshuffler device. Nat. Nanotechnol. 14, 658–661 (2019).

    Article  Google Scholar 

  26. Kim, D.-H. et al. Bulk Dzyaloshinskii–Moriya interaction in amorphous ferrimagnetic alloys. Nat. Mater. 18, 685–690 (2019).

    Article  Google Scholar 

  27. Hirata, Y. et al. Vanishing skyrmion Hall effect at the angular momentum compensation temperature of a ferrimagnet. Nat. Nanotechnol. 14, 232–236 (2019).

    Article  Google Scholar 

  28. Barker, J. & Tretiakov, O. A. Static and dynamical properties of antiferromagnetic skyrmions in the presence of applied current and temperature. Phys. Rev. Lett. 116, 147203 (2016).

    Article  Google Scholar 

  29. Büttner, F., Lemesh, I. & Beach, G. S. D. Theory of isolated magnetic skyrmions: from fundamentals to room temperature applications. Sci. Rep. 8, 4464 (2018).

    Article  Google Scholar 

  30. Bessarab, P. F. et al. Stability and lifetime of antiferromagnetic skyrmions. Phys. Rev. B 99, 140411 (2019).

    Article  Google Scholar 

  31. Finizio, S. et al. Deterministic field-free skyrmion nucleation at a nanoengineered injector device. Nano Lett. 19, 7246–7255 (2019).

    Article  Google Scholar 

  32. Chen, P.-Y., Peng, X. & Yu, S. NeuroSim+: an integrated device-to-algorithm framework for benchmarking synaptic devices and array architectures. In 2017 IEEE International Electron Devices Meeting 6.1.1–6.1.4 (IEEE, 2017).

  33. Garbin, D. et al. HfO2-based OxRAM devices as synapses for convolutional neural networks. IEEE Trans. Electron Devices 62, 2494–2501 (2015).

    Article  Google Scholar 

  34. Tang, J. et al. ECRAM as scalable synaptic cell for high-speed, low-power neuromorphic computing. In 2018 IEEE International Electron Devices Meeting 13.1.1–13.1.4 (2018); https://doi.org/10.1109/IEDM.2018. 8614551

  35. Caretta, L. et al. Fast current-driven domain walls and small skyrmions in a compensated ferrimagnet. Nat. Nanotechnol. 13, 1154–1160 (2018).

    Article  Google Scholar 

  36. Tomasello, R. et al. Electrical detection of single magnetic skyrmion at room temperature. AIP Adv. 7, 056022 (2017).

    Article  Google Scholar 

  37. Ikeda, S. et al. Tunnel magnetoresistance of 604% at 300 K by suppression of Ta diffusion in CoFeB/MgO/CoFeB pseudo-spin-valves annealed at high temperature. Appl. Phys. Lett. 93, 082508 (2008).

    Article  Google Scholar 

  38. Wang, M. et al. Current-induced magnetization switching in atom-thick tungsten engineered perpendicular magnetic tunnel junctions with large tunnel magnetoresistance. Nat. Commun. 9, 671 (2018).

    Article  Google Scholar 

  39. Chen, P. & Yu, S. Compact modeling of RRAM devices and its applications in 1T1R and 1S1R array design. IEEE Trans. Electron Devices 62, 4022–4028 (2015).

    Article  Google Scholar 

  40. Moon, K., Kwak, M., Park, J., Lee, D. & Hwang, H. Improved conductance linearity and conductance ratio of 1T2R synapse device for neuromorphic systems. IEEE Electron Device Lett. 38, 1023–1026 (2017).

    Article  Google Scholar 

  41. Yuasa, S., Hono, K., Hu, G. & Worledge, D. C. Materials for spin-transfer-torque magnetoresistive random-access memory. MRS Bull. 43, 352–357 (2018).

    Article  Google Scholar 

  42. Donahue, M. J. & Porter, D. G. OOMMF User’s Guide, Version 1.0 (NIST, 1999).

  43. Ralph, D. C. & Stiles, M. D. Spin transfer torques. J. Magn. Magn. Mater. 320, 1190–1216 (2008).

    Article  Google Scholar 

  44. Ando, K. Dynamical generation of spin currents. Semicond. Sci. Technol. 29, 043002 (2014).

    Article  Google Scholar 

  45. Sampaio, J., Cros, V., Rohart, S., Thiaville, A. & Fert, A. Nucleation, stability and current-induced motion of isolated magnetic skyrmions in nanostructures. Nat. Nanotechnol. 8, 839–844 (2013).

    Article  Google Scholar 

  46. Tomasello, R. et al. A strategy for the design of skyrmion racetrack memories. Sci. Rep. 4, 6784 (2014).

    Article  Google Scholar 

  47. Zhang, X. et al. Control and manipulation of a magnetic skyrmionium in nanostructures. Phys. Rev. B 94, 094420 (2016).

    Article  Google Scholar 

Download references

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.

Author information

Authors and Affiliations

  1. Center for Spintronics, Korea Institute of Science and Technology, Seoul, Korea

    Kyung Mee Song, Sunkyung Cha, Tae-Eon Park, Kwangsu Kim, Joonyeon Chang & Seonghoon Woo

  2. Center for Opto-Electronic Materials and Devices, Korea Institute of Science and Technology, Seoul, Korea

    Jae-Seung Jeong & Hyunsu Ju

  3. Fert Beijing Institute, BDBC, and School of Microelectronics, Beihang University, Beijing, China

    Biao Pan, Weisheng Zhao & Wang Kang

  4. School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, Guangdong, China

    Xichao Zhang, Jing Xia & Yan Zhou

  5. Department of Physics, University of Ulsan, Ulsan, Korea

    Kwangsu Kim

  6. Swiss Light Source, Paul Scherrer Institut, Villigen, Switzerland

    Simone Finizio & Jörg Raabe

  7. Department of Materials Science & Engineering, Yonsei University, Seoul, Korea

    Joonyeon Chang

  8. IBM Thomas J. Watson Research Center, Yorktown Heights, NY, USA

    Seonghoon Woo

Authors
  1. Kyung Mee Song
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jae-Seung Jeong
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Biao Pan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xichao Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jing Xia
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Sunkyung Cha
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Tae-Eon Park
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Kwangsu Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Simone Finizio
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Jörg Raabe
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Joonyeon Chang
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Yan Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Weisheng Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Wang Kang
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Hyunsu Ju
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Seonghoon Woo
    View author publications

    You can also search for this author in PubMed Google Scholar

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.

Corresponding author

Correspondence to Seonghoon Woo.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41928-020-0385-0

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing