Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

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.

This is a preview of subscription content, access via your institution

Relevant articles

Open Access articles citing this article.

Access options

Rent or buy this article

Get just this article for as long as you need it


Prices may be subject to local taxes which are calculated during checkout

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.


  1. Merkle, R. Energy limits to the computational power of the human brain. Foresight Update 6 (Foresight Institute, 1989);

    Google Scholar 

  2. Laughlin, S. B., de Ruyter van Steveninck, R. R. & Anderson, J. C. The metabolic cost of neural information. Nat. Neurosci. 1, 36–41 (1998).

    Article  CAS  Google Scholar 

  3. Merolla, P. A. et al. A million spiking-neuron integrated circuit with a scalable communication network and interface. Science 345, 668–673 (2014).

    Article  CAS  Google Scholar 

  4. Strukov, D. B., Snider, G. S., Stewart, D. R. & Williams, R. S. The missing memristor found. Nature 453, 80–83 (2008).

    Article  CAS  Google Scholar 

  5. Kim, S. et al. Experimental demonstration of a second-order memristor and its ability to biorealistically implement synaptic plasticity. Nano Lett. 15, 2203–2211 (2015).

    Article  CAS  Google Scholar 

  6. Chortos, A., Liu, J. & Bao, Z. Pursuing prosthetic electronic skin. Nat. Mater. 15, 937–950 (2016).

    Article  CAS  Google Scholar 

  7. Yokota, T. et al. Ultra-flexible organic photonic skin. Sci. Adv. 2, e1501856 (2016).

    Article  Google Scholar 

  8. Burr, G. W. et al. Experimental demonstration and tolerancing of a large-scale neural network (165,000 synapses) using phase-change memory as the synaptic weight element. IEEE Trans. Electron Devices 62, 3498–3507 (2015).

    Article  Google Scholar 

  9. Prezioso, M. et al. Training and operation of an integrated neuromorphic network based on metal-oxide memristors. Nature 521, 61–64 (2015).

    Article  CAS  Google Scholar 

  10. Yang, J. J., Strukov, D. B. & Stewart, D. R. Memristive devices for computing. Nat. Nanotech. 8, 13–24 (2013).

    Article  CAS  Google Scholar 

  11. Wong, H. S. P. et al. Phase change memory. Proc. IEEE 98, 2201–2227 (2010).

    Article  Google Scholar 

  12. Zhou, Y. & Ramanathan, S. Mott memory and neuromorphic devices. Proc. IEEE 103, 1289–1310 (2015).

    Article  CAS  Google Scholar 

  13. Gkoupidenis, P., Schaefer, N., Garlan, B. & Malliaras, G. G. Neuromorphic functions in PEDOT:PSS organic electrochemical transistors. Adv. Mater. 27, 7176–7180 (2015).

    Article  CAS  Google Scholar 

  14. Xu, W., Min, S.-Y., Hwang, H. & Lee, T.-W. Organic core-sheath nanowire artificial synapses with femtojoule energy consumption. Sci. Adv. 2, e1501326 (2016).

    Article  Google Scholar 

  15. Alibart, F. et al. A memristive nanoparticle/organic hybrid synapstor for neuroinspired computing. Adv. Funct. Mater. 22, 609–616 (2012).

    Article  CAS  Google Scholar 

  16. Jonsson, A. et al. Bioelectronic neural pixel: chemical stimulation and electrical sensing at the same site. Proc. Natl Acad. Sci. USA 113, 9440–9445 (2016).

    Article  CAS  Google Scholar 

  17. Larsson, K. C., Kjäll, P. & Richter-Dahlfors, A. Organic bioelectronics for electronic-to-chemical translation in modulation of neuronal signaling and machine-to-brain interfacing. Biochim. Biophys. Acta 1830, 4334–4344 (2013).

    Article  CAS  Google Scholar 

  18. Simon, D. T. et al. An organic electronic biomimetic neuron enables auto-regulated neuromodulation. Biosens. Bioelectron. 71, 359–364 (2015).

    Article  CAS  Google Scholar 

  19. Thakoor, S., Moopenn, A., Daud, T. & Thakoor, A. P. Solid-state thin-film memistor for electronic neural networks. J. Appl. Phys. 67, 3132–3135 (1990).

    Article  CAS  Google Scholar 

  20. Xuan, Y., Sandberg, M., Berggren, M. & Crispin, X. An all-polymer-air PEDOT battery. Org. Electron. Phys. Mater. Appl. 13, 632–637 (2012).

    CAS  Google Scholar 

  21. Bi, G. Q. & Poo, M. M. Synaptic modifications in cultured hippocampal neurons: dependence on spike timing, synaptic strength, and postsynaptic cell type. J. Neurosci. 18, 10464–10472 (1998).

    Article  CAS  Google Scholar 

  22. Jain, V., Yochum, H. M., Montazami, R. & Heflin, J. R. Millisecond switching in solid state electrochromic polymer devices fabricated from ionic self-assembled multilayers. Appl. Phys. Lett. 92, 33304 (2008).

    Article  Google Scholar 

  23. Zucker, R. S. & Regehr, W. G. Short-term synaptic plasticity. Annu. Rev. Physiol. 64, 355–405 (2002).

    Article  CAS  Google Scholar 

  24. Ohno, T. et al. Short-term plasticity and long-term potentiation mimicked in single inorganic synapses. Nat. Mater. 10, 591–595 (2011).

    Article  CAS  Google Scholar 

  25. Kim, I. S. et al. High performance PRAM cell scalable to sub-20 nm technology with below 4F2 cell size, extendable to DRAM applications. 2010 Symposium on VLSI Technology 203–204 (IEEE, 2010).

    Chapter  Google Scholar 

  26. Zhang, Y. et al. 30 nm channel length pentacene transistors. Adv. Mater. 15, 1632–1635 (2003).

    Article  CAS  Google Scholar 

  27. Ziegler, M. et al. An electronic version of Pavlov’s dog. Adv. Funct. Mater. 22, 2744–2749 (2012).

    Article  CAS  Google Scholar 

  28. Bache, K. & Lichman, M. UCI Machine Learning Repository (University of California, School of Information and Computer Science, 2016).

    Google Scholar 

  29. Lecun, Y., Cortes, C. & Burges, C. J. C. Gradient-based learning applied to document recognition. Proc. IEEE 86, 2278–2324 (1998).

    Article  Google Scholar 

  30. Cox, J. A., James, C. D. & Aimone, J. B. A signal processing approach for cyber data classification with deep neural networks. Proc. Comput. Sci. 61, 349–354 (2015).

    Article  Google Scholar 

  31. Bichler, O. et al. Visual pattern extraction using energy-efficient ‘2-PCM Synapse’; Neuromorphic Architecture. IEEE Trans. Electron Devices 59, 2206–2214 (2012).

    Article  Google Scholar 

  32. Stavrinidou, E. et al. Direct measurement of ion mobility in a conducting polymer. Adv. Mater. 25, 4488–4493 (2013).

    Article  CAS  Google Scholar 

Download references


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.

Author information

Authors and Affiliations



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.

Corresponding authors

Correspondence to A. Alec Talin or Alberto Salleo.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Information

Supplementary Information (PDF 1544 kb)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

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).

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI:

This article is cited by


Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing