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Mott neurons with dual thermal dynamics for spatiotemporal computing

Nature Materials volume 23, pages 1237–1244 (2024)Cite this article

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Abstract

Heat dissipation is a natural consequence of operating any electronic system. In nearly all computing systems, such heat is usually minimized by design and cooling. Here, we show that the temporal dynamics of internally produced heat in electronic devices can be engineered to both encode information within a single device and process information across multiple devices. In our demonstration, electronic NbOx Mott neurons, integrated on a flexible organic substrate, exhibit 18 biomimetic neuronal behaviours and frequency-based nociception within a single component by exploiting both the thermal dynamics of the Mott transition and the dynamical thermal interactions with the organic substrate. Further, multiple interconnected Mott neurons spatiotemporally communicate purely via heat, which we use for graph optimization by consuming over 106 times less energy when compared with the best digital processors. Thus, exploiting natural thermal processes in computing can lead to functionally dense, energy-efficient and radically novel mixed-physics computing primitives.

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Fig. 1: Flexible biomimetic neuron.
Fig. 2: The heat-storable TS behaviours on a flexible PI substrate and its various neuronal behaviours.
Fig. 3: Spiking nociceptive behaviours.
Fig. 4: Spatiotemporal sparse thermal computing in Mott neural network.

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All data that support the findings of this study are reported in the Article and its Supplementary Information.

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Acknowledgements

Work by G.K., J.H.I., Y.L., H.R., W.P., H.S., J.P., J.B.J. and K.M.K. was supported by the National Research Foundation of Korea (NRF) (grant numbers RS-2023-00216619, RS-2023-00216992, 2022M3F3A2A01076569, 2022M3I7A40854842022M3I7A408548411, 2023R1A2C2005159 and 2023R1A2C200515911), NNFC (grant number 1711160154) and the UP programme of KAIST (grant number N10230061). Work by T.D.B., A.A.T. and S.K. was primarily supported as part of the Center for Reconfigurable Electronic Materials Inspired by Nonlinear Neuron Dynamics (reMIND), an Energy Frontier Research Center funded by the US Department of Energy (DOE), Office of Science, Basic Energy Sciences. Institutional support was received from the Laboratory-Directed R&D programme of Sandia National Laboratories. Sandia National Laboratories is a multimission laboratory operated for the US Department of Energy (DOE)’s National Nuclear Security Administration under contract DE-NA0003525. Any subjective views or opinions that might be expressed in the paper do not necessarily represent the views of the US Department of Energy or the United States Government.

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Authors and Affiliations

  1. Department of Materials Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea

    Gwangmin Kim, Jae Hyun In, Younghyun Lee, Hakseung Rhee, Woojoon Park, Hanchan Song, Juseong Park, Jae Bum Jeon & Kyung Min Kim

  2. Sandia National Laboratories, Livermore, CA, USA

    Timothy D. Brown, A. Alec Talin & Suhas Kumar

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Contributions

G.K. conceived the third-order system of the device. G.K., J.H.I. and Y.L. fabricated the devices and performed the experiments. G.K. conducted the COMSOL, numerical and spatiotemporal thermal computing simulations. H.R. and S.K. suggested thermal computing concepts. G.K. and S.K. performed the benchmarking calculations. S.K., A.A.T. and T.D.B. performed the thermal microscopy measurements. H.R., W.P., H.S., J.P. and J.B.J. helped with data analysis and discussed the results. G.K., J.H.I., Y.L., S.K. and K.M.K. wrote the manuscript. All the authors commented on the manuscript. S.K. and K.M.K. supervised this study. G.K., J.H.I. and Y.L. contributed equally to this work.

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Correspondence to Suhas Kumar or Kyung Min Kim.

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Nature Materials thanks Mario Lanza and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Supplementary Figs. 1–19, Tables 1–3 and Notes 1–4.

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Kim, G., In, J.H., Lee, Y. et al. Mott neurons with dual thermal dynamics for spatiotemporal computing. Nat. Mater. 23, 1237–1244 (2024). https://doi.org/10.1038/s41563-024-01913-0

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