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A non-volatile organic electrochemical device as a low-voltage artificial synapse for neuromorphic computing


The brain is capable of massively parallel information processing while consuming only 1–100 fJ per synaptic event1,2. Inspired by the efficiency of the brain, CMOS-based neural architectures3 and memristors4,5 are being developed for pattern recognition and machine learning. However, the volatility, design complexity and high supply voltages for CMOS architectures, and the stochastic and energy-costly switching of memristors complicate the path to achieve the interconnectivity, information density, and energy efficiency of the brain using either approach. Here we describe an electrochemical neuromorphic organic device (ENODe) operating with a fundamentally different mechanism from existing memristors. ENODe switches at low voltage and energy (<10 pJ for 103 μm2 devices), displays >500 distinct, non-volatile conductance states within a 1 V range, and achieves high classification accuracy when implemented in neural network simulations. Plastic ENODes are also fabricated on flexible substrates enabling the integration of neuromorphic functionality in stretchable electronic systems6,7. Mechanical flexibility makes ENODes compatible with three-dimensional architectures, opening a path towards extreme interconnectivity comparable to the human brain.

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Figure 1: Structure and electronic states of an organic neuromorphic device.
Figure 2: Neuromorphic behaviour.
Figure 3: Flexible all solid-state neuromorphic device.
Figure 4: Learning circuit and image recognition simulations.


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A.S. gratefully acknowledges financial support from the National Science Foundation (Award #DMR 1507826). Y.v.d.B. was supported by the Keck Faculty Scholar Funds and the Neurofab at Stanford. S.T.K. was supported by the Stanford Graduate Fellowship fund. Help from J. Rivnay in making small devices is gratefully acknowledged. This work was supported in part by Sandia’s Laboratory-Directed Research and Development (LDRD) Program under the Hardware Acceleration of Adaptive Neural Algorithms (HAANA) Grand Challenge. E.J.F. and A.A.T. were also supported by Nanostructures for Electrical Energy Storage (NEES-II), an Energy Frontier Research Center funded by the US Department of Energy, Office of Science, Basic Energy Sciences under Award number DESC0001160. Sandia National Laboratories is a multi-mission laboratory managed and operated by Sandia Corporation, a wholly owned subsidiary of Lockheed Martin Corporation, for the US Department of Energy’s National Nuclear Security Administration under contract DE-AC0494AL85000. E.L. also acknowledges the support of Holland Scholarship, University of Groningen Scholarship for Excellent Students, Hendrik Muller Vaderlandschfonds, Schimmel Schuurman van Outerenstichting, Fundatie Vrijvrouwe van Renswoude te Delft, Fundatie Vrijvrouwe van Renswoude te ’s Gravenhage, Marco Polo fund. G.C.F. acknowledges INCT/INEO, the Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) and the Brazilian National Council (CNPq/Science without Borders Project) for financial support through project numbers 2013/21034-0 and 201753/2014-6.

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Y.v.d.B., E.L., A.A.T. and A.S. designed the experiments. Y.v.d.B., E.L., E.J.F., S.T.K. and G.C.F. collected and analysed data. S.A. and M.J.M. designed the back-propagation simulations. All authors discussed the results and contributed to the manuscript preparation. Y.v.d.B., E.L., E.J.F., A.A.T. and A.S. wrote the manuscript.

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Correspondence to A. Alec Talin or Alberto Salleo.

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The authors declare no competing financial interests.

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van de Burgt, Y., Lubberman, E., Fuller, E. et al. A non-volatile organic electrochemical device as a low-voltage artificial synapse for neuromorphic computing. Nature Mater 16, 414–418 (2017).

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