Journal
NATURE MATERIALS
Volume 16, Issue 4, Pages 414-+Publisher
NATURE PUBLISHING GROUP
DOI: 10.1038/NMAT4856
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Funding
- National Science Foundation [DMR 1507826]
- Keck Faculty Scholar Funds
- Neurofab at Stanford
- Stanford Graduate Fellowship
- Sandia's Laboratory-Directed Research and Development (LDRD) Program under the Hardware Acceleration of Adaptive Neural Algorithms (HAANA) Grand Challenge
- Nanostructures for Electrical Energy Storage (NEES-II), an Energy Frontier Research Center - US Department of Energy, Office of Science, Basic Energy Sciences [DESC0001160]
- US Department of Energy's National Nuclear Security Administration [DE-AC0494AL85000]
- 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
- INCT/INEO
- Fundacao de Amparo a Pesquisa do Estado de Sao Paulo (FAPESP)
- Brazilian National Council (CNPq/Science without Borders Project) [2013/21034-0, 201753/2014-6]
- Division Of Materials Research
- Direct For Mathematical & Physical Scien [1507826] Funding Source: National Science Foundation
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The brain is capable of massively parallel information processing while consuming only similar to 1-100 fJ per synaptic event(1,2). Inspired by the efficiency of the brain, CMOS-based neural architectures(3) and memristors(4,5) 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 10(3) mu m(2) devices), displays >500 distinct, non-volatile conductance states within a similar to 1V 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 systems(6,7). Mechanical flexibility makes ENODes compatible with three-dimensional architectures, opening a path towards extreme interconnectivity comparable to the human brain.
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