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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 energy (<10 pJ for 10 3 μm 2 devices) and voltage, 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 3D architectures, opening a path towards extreme interconnectivity comparable to the human brain.

Authors:
ORCiD logo [1];  [2];  [3];  [1];  [4];  [3];  [5];  [3];  [1]
  1. Stanford Univ., CA (United States)
  2. Stanford Univ., CA (United States); Univ. of Groningen (Netherlands)
  3. Sandia National Lab. (SNL-CA), Livermore, CA (United States)
  4. Stanford Univ., CA (United States); Univ. of Sao Paulo (Brazil)
  5. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Publication Date:
Research Org.:
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Basic Energy Sciences (BES) (SC-22)
OSTI Identifier:
1365809
Report Number(s):
SAND-2017-1662J
Journal ID: ISSN 1476-1122; nmat4856
Grant/Contract Number:  
AC04-94AL85000
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Nature Materials
Additional Journal Information:
Journal Volume: 16; Journal Issue: 4; Journal ID: ISSN 1476-1122
Publisher:
Nature Publishing Group
Country of Publication:
United States
Language:
English
Subject:
36 MATERIALS SCIENCE

Citation Formats

van de Burgt, Yoeri, Lubberman, Ewout, Fuller, Elliot J., Keene, Scott T., Faria, Gregorio C., Agarwal, Sapan, Marinella, Matthew J., Alec Talin, A., and Salleo, Alberto. A non-volatile organic electrochemical device as a low-voltage artificial synapse for neuromorphic computing. United States: N. p., 2017. Web. doi:10.1038/nmat4856.
van de Burgt, Yoeri, Lubberman, Ewout, Fuller, Elliot J., Keene, Scott T., Faria, Gregorio C., Agarwal, Sapan, Marinella, Matthew J., Alec Talin, A., & Salleo, Alberto. A non-volatile organic electrochemical device as a low-voltage artificial synapse for neuromorphic computing. United States. doi:10.1038/nmat4856.
van de Burgt, Yoeri, Lubberman, Ewout, Fuller, Elliot J., Keene, Scott T., Faria, Gregorio C., Agarwal, Sapan, Marinella, Matthew J., Alec Talin, A., and Salleo, Alberto. Mon . "A non-volatile organic electrochemical device as a low-voltage artificial synapse for neuromorphic computing". United States. doi:10.1038/nmat4856. https://www.osti.gov/servlets/purl/1365809.
@article{osti_1365809,
title = {A non-volatile organic electrochemical device as a low-voltage artificial synapse for neuromorphic computing},
author = {van de Burgt, Yoeri and Lubberman, Ewout and Fuller, Elliot J. and Keene, Scott T. and Faria, Gregorio C. and Agarwal, Sapan and Marinella, Matthew J. and Alec Talin, A. and Salleo, Alberto},
abstractNote = {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 energy (<10 pJ for 103 μm2 devices) and voltage, 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 3D architectures, opening a path towards extreme interconnectivity comparable to the human brain.},
doi = {10.1038/nmat4856},
journal = {Nature Materials},
number = 4,
volume = 16,
place = {United States},
year = {Mon Feb 20 00:00:00 EST 2017},
month = {Mon Feb 20 00:00:00 EST 2017}
}

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