A non-volatile organic electrochemical device as a low-voltage artificial synapse for neuromorphic computing.
Open accessAbstract
The brain is capable of massively parallel information processing while consuming only ∼1-100 fJ per synaptic event. Inspired by the efficiency of the brain, CMOS-based neural architectures and memristors 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 μm 2 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 systems. Mechanical flexibility makes ENODes compatible with three-dimensional architectures, opening a path towards extreme interconnectivity comparable to the human brain.
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A recently developed ultra-low-power artificial synapse for neural computing has demonstrated the capacity to provide 500 distinct states.
“A recently developed ultra-low-power artificial synapse for neural computing has demonstrated the capacity to provide 500 distinct states”
From Human Brain/Cloud Interface (2019) · cites this paper as Van de Burgt et al., 2017
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Self-citation
- Authors
- van de Burgt, Y., Lubberman, E., Fuller, E. J., Keene, S. T., Faria, G. C., Agarwal, S., Marinella, M. J., Alec Talin, A., & Salleo, A.
- Affiliations
- University of Groningen (NL)
- Sandia National Laboratories California (US)
- Stanford University (US)
- Sandia National Laboratories (US)
- Corresponding author
- Salleo, Alberto
- ORCID
- Venue
- Nature Materials
- Published
- 2017-02-20
- Volume
- 16
- Issue
- 4
- Pages
- 414-418
- ISSN
- 1476-1122 (ISSN-L)
- 1476-4660
- OpenAlex
- W2591029953
- Cited by
- 1,872
- References
- 7
- Citations by year
- 2026: 178 · 2025: 247 · 2024: 217 · 2023: 235 · 2022: 255 · 2021: 232 · 2020: 207 · 2019: 158 · 2018: 117 · 2017: 25
- FWCI
- 57.65
- Type
- journal article
- Language
- en
- Open access
- green
- research.tue.nl
- Licence
- Locations
- Nature Materials (publishedVersion)
- PubMed (publishedVersion)
- TU/e Research Portal (publishedVersion, other-oa)
- OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
- TU/e Research Portal (submittedVersion, other-oa)
- TU/e Research Portal (submittedVersion)
- Cambridge University Engineering Department Publications Database (submittedVersion)
- University of Groningen research database (University of Groningen / Centre for Information Technology) (submittedVersion, other-oa)
- Funders
- National Science Foundation (1507826)
- U.S. Department of Energy (DE–AC04–94AL85000, DESC0001160)
- Rijksuniversiteit Groningen
- Fundação de Amparo à Pesquisa do Estado de São Paulo (201753/2014-6, 2013/ 21034-0)
- Conselho Nacional de Desenvolvimento Científico e Tecnológico (201753/2014-6)
- Instituto Nacional de Ciência e Tecnologia em Eletrônica Orgânica (2013/21034-0)
- Office of Science (DESC0001160)
- National Nuclear Security Administration (DE-AC04- 94AL85000)
- Ciência sem Fronteiras
- Division of Materials Research (DMR 1507826)
- Basic Energy Sciences (DE-SC00-01160)
- Laboratory Directed Research and Development
- Sandia National Laboratories
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Memory and Neural Computing
- Physical Sciences › Materials Science › Polymers and Plastics › Conducting polymers and applications
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Ferroelectric and Negative Capacitance Devices
- Keywords
- artificial synapses, neuromorphic computing, non-volatile memory, low-voltage switching, conductance states, memristors, flexible electronics, stretchable electronics, energy-efficient neuromorphic hardware, neural network classification
- MeSH terms
- Brain, Humans, Nerve Net, Computers, Molecular, Electrochemical Techniques
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