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Organic electronics for neuromorphic computing

Abstract

Neuromorphic computing could address the inherent limitations of conventional silicon technology in dedicated machine learning applications. Recent work on silicon-based asynchronous spiking neural networks and large crossbar arrays of two-terminal memristive devices has led to the development of promising neuromorphic systems. However, delivering a compact and efficient parallel computing technology that is capable of embedding artificial neural networks in hardware remains a significant challenge. Organic electronic materials offer an attractive option for such systems and could provide biocompatible and relatively inexpensive neuromorphic devices with low-energy switching and excellent tunability. Here, we review the development of organic neuromorphic devices. We consider different resistance-switching mechanisms, which typically rely on electrochemical doping or charge trapping, and report approaches that enhance state retention and conductance tuning. We also discuss the challenges the field faces in implementing low-power neuromorphic computing, such as device downscaling and improving device speed. Finally, we highlight early demonstrations of device integration into arrays, and consider future directions and potential applications of this technology.

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Acknowledgements

The authors would like to thank M. Marinella and S. Agarwal from Sandia National Labs for help in preparing this document. A.M. gratefully acknowledges support from the Knut and Alice Wallenberg Foundation (KAW 2016.0494) for postdoctoral research at Stanford University. S.T.K. was funded by the Stanford Graduate Fellowship.

Author information

Y.v.d.B. collected all the articles and data. Y.v.d.B., A.M. and S.K. wrote the manuscript. All authors contributed to the discussion and writing.

Competing interests

The authors declare no competing interests.

Correspondence to Yoeri van de Burgt or Armantas Melianas.

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Further reading

Fig. 1: Overview of conductance switching mechanisms in organic electronic materials.
Fig. 2: Conductance tuning methods for electrolyte-gated redox-based neuromorphic devices.

reproduced from ref. 84, Springer Nature Ltd.

Fig. 3: Non-volatility in electrolyte-gated redox-based neuromorphic devices.
Fig. 4: Examples of integration and functionality.

reproduced from ref. 105, Springer Nature Ltd (a); ref. 103, Springer Nature Ltd (b); ref. 89, Springer Nature Ltd (c); ref. 79, Elsevier (d); and ref. 109, AAAS (e)