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  • Technical Review
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Metrics for spin-based computing

Nature Reviews Physics volume 8, pages 208–225 (2026) Cite this article

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Abstract

Spin-based computing is emerging as a powerful approach for energy-efficient and high-performance solutions to future data processing hardware. Spintronic devices function by electrically manipulating the collective dynamics of the electron spin, which is inherently non-volatile, nonlinear and fast operating, and can couple to other degrees of freedom such as photonic and phononic systems. This Technical Review explores key advances in integrating magnetic and spintronic elements into computational architectures, ranging from fundamental components such as radiofrequency neurons or synapses and spintronic probabilistic bits to broader frameworks such as reservoir computing and magnetic Ising machines. For each of these systems, we discuss hardware-specific and task-dependent metrics to evaluate their computing performance and evaluate the physical processes that need to be optimized to increase performance. Finally, we discuss challenges and future opportunities, highlighting the potential of spin-based computing in next-generation technologies.

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Fig. 1: Conceptual diagrams of spin-based computing technologies.
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Fig. 2: Types of stochastic magnetic tunnel junction design.
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Fig. 3: Reservoir computing metrics evaluations.
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Fig. 4: SWIM time traces for the measurement of time-to-saturation τsat and time-to-solution τsolution parameters.
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Acknowledgements

H.K. thanks the Leverhulme Trust for financial support via their Research Fellowship (RF-2024-317) and JSPS for their support through Kakenhi (grant no. 25H00837). H.K. and J.C.G. were supported by EPSRC grant EP/X015661/1. J.C.G. was supported by EPSRC grant EP/Y003276/1, the Royal Academy of Engineering Fellowship RF2122-21-363, the ERC Starting Grant MORPHON, and the Imperial College London President's Excellence Fund for Frontier Research. A.L. acknowledges funding from the Marie Skłodowska-Curie grant agreement no. 101111429 “SWIM”. J.Å. acknowledges funding from the Horizon 2020 research and innovation programme no. 835068 “TOPSPIN” and as a Swedish Research Council Distinguished Professor (Dnr. 2024-01943). K.E.-S. acknowledges funding from the Emergent AI Center funded by the Carl Zeiss Foundation and the German Research Foundation (DFG) Project ID 403233384 (SPP Skyrmionics) and 405553726 (CRC/TRR 270, project B12). S.F. acknowledges funding from JST-ASPIRE (grant no. JPMJAP2322), JST-CREST (grant no. JPMJCR19K3), and JSPS Kakenhi (grant nos. 24H00039, 24H02235 and 25H00447). K.Y.Ç. and K.S. acknowledge support from the Office of Naval Research (ONR), Multidisciplinary University Research Initiative (MURI) grant N000142312708. T.T. acknowledges JSPS Kakenhi (grant no. 24K01336). The authors thank J. Grollier and F. Mizrahi for their help on the initial stage of manuscript writing, and M. Stiles and D. Gopman for their feedback.

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Authors and Affiliations

  1. London Centre for Nanotechnology, University College London, London, UK

    Hidekazu Kurebayashi

  2. Department of Electronic and Electrical Engineering, University College London, London, UK

    Hidekazu Kurebayashi

  3. WPI Advanced Institute for Materials Research (WPI-AIMR), Tohoku University, Sendai, Japan

    Hidekazu Kurebayashi & Shunsuke Fukami

  4. Center for Science and Innovation in Spintronics (CSIS), Tohoku University, Sendai, Japan

    Hidekazu Kurebayashi, Akash Kumar, Johan Åkerman & Shunsuke Fukami

  5. Institute for Materials Research, Tohoku University, Sendai, Japan

    Hidekazu Kurebayashi

  6. Department of Mathematical and Computer Sciences, Physical Sciences and Earth Sciences, University of Messina, Messina, Italy

    Giovanni Finocchio

  7. Faculty of Physics and Center for Nanointegration Duisburg-Essen (CENIDE), University of Duisburg-Essen, Duisburg, Germany

    Karin Everschor-Sitte

  8. Blackett Laboratory, Imperial College London, London, UK

    Jack C. Gartside

  9. National Institute of Advanced Industrial Science and Technology (AIST), Research Center for Emerging Computing Technologies, Tsukuba, Japan

    Tomohiro Taniguchi

  10. Department of Physics, University of Gothenburg, Gothenburg, Sweden

    Artem Litvinenko, Akash Kumar & Johan Åkerman

  11. Research Institute of Electrical Communication (RIEC), Tohoku University, Sendai, Japan

    Akash Kumar, Johan Åkerman & Shunsuke Fukami

  12. University of Sheffield, Sheffield, UK

    Eleni Vasilaki

  13. Department of Electrical and Computer Engineering, University of California, Santa Barbara, Santa Barbara, CA, USA

    Kemal Selçuk & Kerem Y. Çamsarı

  14. National Institute of Standards and Technology, Gaithersburg, MD, USA

    Advait Madhavan

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H.K., G.F. and S.F. initiated this project and all authors contributed to the preparation of the article.

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Kurebayashi, H., Finocchio, G., Everschor-Sitte, K. et al. Metrics for spin-based computing. Nat Rev Phys 8, 208–225 (2026). https://doi.org/10.1038/s42254-025-00918-1

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