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Multilayer spintronic neural networks with radiofrequency connections

Nature Nanotechnology volume 18, pages 1273–1280 (2023)Cite this article

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Abstract

Spintronic nano-synapses and nano-neurons perform neural network operations with high accuracy thanks to their rich, reproducible and controllable magnetization dynamics. These dynamical nanodevices could transform artificial intelligence hardware, provided they implement state-of-the-art deep neural networks. However, there is today no scalable way to connect them in multilayers. Here we show that the flagship nano-components of spintronics, magnetic tunnel junctions, can be connected into multilayer neural networks where they implement both synapses and neurons thanks to their magnetization dynamics, and communicate by processing, transmitting and receiving radiofrequency signals. We build a hardware spintronic neural network composed of nine magnetic tunnel junctions connected in two layers, and show that it natively classifies nonlinearly separable radiofrequency inputs with an accuracy of 97.7%. Using physical simulations, we demonstrate that a large network of nanoscale junctions can achieve state-of-the-art identification of drones from their radiofrequency transmissions, without digitization and consuming only a few milliwatts, which constitutes a gain of several orders of magnitude in power consumption compared to currently used techniques. This study lays the foundation for deep, dynamical, spintronic neural networks.

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Fig. 1: Building blocks of multilayer RF/d.c. spintronic neural networks.
Fig. 2: Experimental demonstration of neurons-to-synapses interconnection.
Fig. 3: Experimental demonstration of the spintronic neural network.
Fig. 4: Drone classification by direct processing of their RF emissions.

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Data availability

The data supporting the figures of this study are publicly available in the Zenodo repository: https://doi.org/10.5281/zenodo.7956045.

Code availability

The source codes used in this study, in particular to generate the data of Fig. 4, are publicly available in the Github repository: https://github.com/neurophysics-cnrsthales/rf_spintronic_nn.

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Acknowledgements

This work was supported by the European Union’s Horizon 2020 research and innovation programme under grant RadioSpin no. 101017098. The text of the article was partially edited by a large language model (OpenAI ChatGPT).

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  1. These authors contributed equally: Andrew Ross, Nathan Leroux.

Authors and Affiliations

  1. Unité Mixte de Physique CNRS/Thales, CNRS, Thales, Université Paris-Saclay, Palaiseau, France

    Andrew Ross, Nathan Leroux, Arnaud De Riz, Danijela Marković, Dédalo Sanz-Hernández, Juan Trastoy, Paolo Bortolotti, Frank Alice Mizrahi & Julie Grollier

  2. Centre de Nanosciences et de Nanotechnologies, Université Paris-Saclay, CNRS, Palaiseau, France

    Damien Querlioz

  3. International Iberian Nanotechnology Laboratory (INL), Braga, Portugal

    Leandro Martins, Luana Benetti, Marcel S. Claro, Pedro Anacleto, Alejandro Schulman, Alex S. Jenkins & Ricardo Ferreira

  4. Laboratoire de l’Intégration du Matériau au Système (IMS; UMR 5218), Univ. Bordeaux, CNRS, Bordeaux INP, Talence, France

    Thierry Taris, Jean-Baptiste Begueret, Sylvain Saïghi & Adrien F. Vincent

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Contributions

The study was designed and supervised by J.G. and F.A.M.; samples were optimized and fabricated by R.F., A.S.J., L.M., L.B., M.S.C., P.A., A.S. and A.S.J.; experiments and training of the hardware neural network were performed by F.A.M., A.R., N.L., D.M., J.T. and D.S.-H.; simulations of neural networks and drone classification were performed by F.A.M., A.d.R. and N.L.; the estimation of scaled network performances was performed by F.A.M., N.L. and A.F.V.; and F.A.M., P.B., D.Q., T.T., J.-B.B. and S.S. provided insights on applications and circuit design. All authors contributed to the analysis of the results and writing of the paper.

Corresponding authors

Correspondence to Frank Alice Mizrahi or Julie Grollier.

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Competing interests

The authors declare the following competing interests: patents FR 1800805, 1800806 and 1800807 are held by CNRS and Thales, and J.G. is the inventor. They cover the neural network architecture proposed in this work. The remaining authors declare no competing interests.

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Nature Nanotechnology thanks Jianhua Yang, Thomas Hayward and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Supplementary Figs. 1–10 and Discussion.

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Ross, A., Leroux, N., De Riz, A. et al. Multilayer spintronic neural networks with radiofrequency connections. Nat. Nanotechnol. 18, 1273–1280 (2023). https://doi.org/10.1038/s41565-023-01452-w

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