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Intrinsic plasticity of silicon nanowire neurotransistors for dynamic memory and learning functions

A Publisher Correction to this article was published on 16 June 2020

This article has been updated

Abstract

Neuromorphic architectures merge learning and memory functions within a single unit cell and in a neuron-like fashion. Research in the field has been mainly focused on the plasticity of artificial synapses. However, the intrinsic plasticity of the neuronal membrane is also important in the implementation of neuromorphic information processing. Here we report a neurotransistor made from a silicon nanowire transistor coated by an ion-doped sol–gel silicate film that can emulate the intrinsic plasticity of the neuronal membrane. The neurotransistors are manufactured using a conventional complementary metal–oxide–semiconductor process on an 8-inch (200 mm) silicon-on-insulator wafer. Mobile ions allow the film to act as a pseudo-gate that generates memory and allows the neurotransistor to display plasticity. We show that multiple pulsed input signals of the neurotransistor are non-linearly processed by sigmoidal transformation into the output current, which resembles the functioning of a neuronal membrane. The output response is governed by the input signal history, which is stored as ionic states within the silicate film, and thereby provides the neurotransistor with learning capabilities.

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Fig. 1: Structure and electrical characteristics of Si NW neurotransistors.
Fig. 2: Intrinsic plasticity of neurotransistors.
Fig. 3: Non-linear information processing of the neurotransistor with multiple inputs and outputs.
Fig. 4: History-dependent dynamic memory and learning within a single neurotransistor and its network.

Data availability

The source data for Figs. 14 are available within the paper and its Supplementary Information.

Code availability

The MatLab code that supports the mathematical model in this article is available at https://github.com/eunhye8747/MatLab-Code-Neurotransistor.

Change history

  • 16 June 2020

    An amendment to this paper has been published and can be accessed via a link at the top of the paper.

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Acknowledgements

This research was supported by the German Excellence Initiative via the Cluster of Excellence EXC1056 Center for Advancing Electronics Dresden (CfAED) and the MSIP (Ministry of Science, ICT and Future Planning), Korea, under the ICT Consilience Creative Program (IITP-R0346-16-1007) supervised by the IITP (Institute for Information & communications Technology Promotion). We acknowledge support from the Initiative and Networking Fund of the Helmholtz Association of German Research Centers through the International Helmholtz Research School for Nanoelectronic Networks (IHRS NANONET) (no. VH‐KO‐606) and German Research Foundation (DFG) via grants MA 5144/9-1, MA 5144/13-1 and MA 5144/14-1; BA4986/7−1, BA4986/8−1. Finally, we thank the INSA-DFG Bilateral Exchange Programme for financial support (IA/DFG/2018/138, 12 April 2018). The authors thank S. Oswald (IFW Dresden) for the X-ray photoemission spectroscopy analysis of the ion-doped hybrid silicate films and M. Park (NamLab, Dresden) for the insightful discussion about the ionic polarization in the film. We thank R. Nigmetzianov (TU Dresden) for the film analysis.

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E.B. developed the idea of neurotransistors and their network for the supervised learning tasks, designed corresponding experiments and analysed the data. N.R.D. designed the LT-SPICE circuit simulations and mathematical models derived from the physics of the neurotransistor. C.V.C. figured out the sigmoidal growth of the output curve and its functionality in the single neural network, and discovered the correlation between the intrinsic plasticity of the device and the neuronal membrane. K.N. developed the hardware for network applications of neurotransistors. T.R. designed and supplied the Si NW FET devices. G.S.C.B. and D.M. provided support in the pulse measurements. H.C. and K.K. fabricated the transistors and revised the manuscript. C.-K.B. supported the research stay of E.B. for the electrical analysis of the device in POSTECH. R.T. and L.C. devised ideas to model the neurotransistors and to realize the single-layer neural network. L.B. supervised the research and contributed to the discussion of the overall methodology and results. G.C. contributed to the discussion and supporting network and infrastructure of the research. E.B., C.V.C., D.M., L.B. and G.C. co-wrote and modified the manuscript.

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Correspondence to Eunhye Baek or Larysa Baraban or Gianaurelio Cuniberti.

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Source Data Fig. 2

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Source Data Fig. 4

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Baek, E., Das, N.R., Cannistraci, C.V. et al. Intrinsic plasticity of silicon nanowire neurotransistors for dynamic memory and learning functions. Nat Electron 3, 398–408 (2020). https://doi.org/10.1038/s41928-020-0412-1

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