title: A non-volatile organic electrochemical device as a low-voltage artificial synapse for neuromorphic computing.
abstract: 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 103μm2devices), 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.
authors.affiliation: Department of Materials Science and Engineering, Stanford University, Stanford, California 94305, USA.
authors.forename: Yoeri
authors.identifier: 0000-0003-3472-0148
authors.initials: Y
authors.lastname: van de Burgt
authors.affiliation: Department of Materials Science and Engineering, Stanford University, Stanford, California 94305, USA.|Zernike Institute for Advanced Materials, University of Groningen, 9747AG Gronigen, The Netherlands.
authors.forename: Ewout
authors.initials: E
authors.lastname: Lubberman
authors.affiliation: Sandia National Laboratories, Livermore, California 94551, USA.
authors.forename: Elliot J
authors.initials: EJ
authors.lastname: Fuller
authors.affiliation: Department of Materials Science and Engineering, Stanford University, Stanford, California 94305, USA.
authors.forename: Scott T
authors.initials: ST
authors.lastname: Keene
authors.affiliation: Department of Materials Science and Engineering, Stanford University, Stanford, California 94305, USA.|Instituto de Física de São Carlos, Universidade de São Paulo, 13566-590 São Carlos, SP, Brasil.
authors.forename: Grégorio C
authors.initials: GC
authors.lastname: Faria
authors.affiliation: Sandia National Laboratories, Livermore, California 94551, USA.
authors.forename: Sapan
authors.initials: S
authors.lastname: Agarwal
authors.affiliation: Sandia National Laboratories, Albuquerque, New Mexico 87123, USA.
authors.forename: Matthew J
authors.initials: MJ
authors.lastname: Marinella
authors.affiliation: Sandia National Laboratories, Livermore, California 94551, USA.
authors.forename: A
authors.initials: A
authors.lastname: Alec Talin
authors.affiliation: Department of Materials Science and Engineering, Stanford University, Stanford, California 94305, USA.
authors.forename: Alberto
authors.initials: A
authors.lastname: Salleo
mesh_terms: D001921:Brain; D039301:Computers, Molecular; D055664:Electrochemical Techniques; D006801:Humans; D009415:Nerve Net