Third-order nanocircuit elements for neuromorphic engineering

Abstract

Current hardware approaches to biomimetic or neuromorphic artificial intelligence rely on elaborate transistor circuits to simulate biological functions. However, these can instead be more faithfully emulated by higher-order circuit elements that naturally express neuromorphic nonlinear dynamics1,2,3,4. Generating neuromorphic action potentials in a circuit element theoretically requires a minimum of third-order complexity (for example, three dynamical electrophysical processes)5, but there have been few examples of second-order neuromorphic elements, and no previous demonstration of any isolated third-order element6,7,8. Using both experiments and modelling, here we show how multiple electrophysical processes—including Mott transition dynamics—form a nanoscale third-order circuit element. We demonstrate simple transistorless networks of third-order elements that perform Boolean operations and find analogue solutions to a computationally hard graph-partitioning problem. This work paves a way towards very compact and densely functional neuromorphic computing primitives, and energy-efficient validation of neuroscientific models.

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Fig. 1: Element construction and static measurements.
Fig. 2: Experimental measurements and modelling of action potentials.
Fig. 3: Experimental demonstration of universal Boolean logic via nonmonotonic spiking behaviour.
Fig. 4: Experimental demonstration of neuromorphic analogue computing.

Data availability

Data presented in the main figures of the manuscript are available from the authors upon reasonable request.

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Acknowledgements

We thank L. O. Chua and H. S. P. Wong for comments on the manuscript. We also thank K. J. Cremata, A. L. D. Kilcoyne, T. Tyliszczak, D. Shapiro, G. Gibson, X. Sheng and J. Zhang for assistance in collecting experimental data or construction of the models. We acknowledge S. M. Bohaichuk, J. C. Nino, A. Conklin and J. L. Andrews for discussions on specific topics and suggestions for illustrations. Work was performed in part in the nano@Stanford labs, which are supported by the National Science Foundation under award ECCS-1542152. Synchrotron measurements were conducted at the Advanced Light Source, a US DOE Office of Science User Facility under contract no. DE-AC02-05CH11231, and at the Stanford Synchrotron Radiation Lightsource, SLAC National Accelerator Laboratory, a US DOE Office of Science User Facility under contract no. DE-AC02-76SF00515. R.S.W. acknowledges the X-Grants Program of the President’s Excellence Fund at Texas A&M University.

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Contributions

S.K. and R.S.W. conceived the project and planned the various measurements. Z.W. performed the materials growth, compositional analysis and parts of the chip fabrication. S.K. and Z.W. performed the in operando spectroscopic measurements and collected the electrical data. S.K. and R.S.W. constructed the model and wrote the manuscript.

Corresponding author

Correspondence to Suhas Kumar.

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Peer review information Nature thanks Adnan Mehonic, Syed Ghazi Sarwat and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Supplementary information

Supplementary Information

9 sections of discussion and 31 figures. Document contains additional information on fabrication and construction, material and electrical measurements, data analysis, compact model and other general discussions supporting the main text.

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Kumar, S., Williams, R.S. & Wang, Z. Third-order nanocircuit elements for neuromorphic engineering. Nature 585, 518–523 (2020). https://doi.org/10.1038/s41586-020-2735-5

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