All-Solid-State Synaptic Transistor with Ultralow Conductance for Neuromorphic Computing
Abstract
Electronic synaptic devices are important building blocks for neuromorphic computational systems that can go beyond the constraints of von Neumann architecture. Although two-terminal memristive devices are demonstrated to be possible candidates, they suffer from several shortcomings related to the filament formation mechanism including nonlinear switching, write noise, and high device conductance, all of which limit the accuracy and energy efficiency. Electrochemical three-terminal transistors, in which the channel conductance can be tuned without filament formation provide an alternative platform for synaptic electronics. In this work, an all-solid-state electrochemical transistor made with Li ion–based solid dielectric and 2D α-phase molybdenum oxide (α-MoO3) nanosheets as the channel is demonstrated. These devices achieve nonvolatile conductance modulation in an ultralow conductance regime (<75 nS) by reversible intercalation of Li ions into the α-MoO3 lattice. Based on this operating mechanism, the essential functionalities of synapses, such as short- and long-term synaptic plasticity and bidirectional near-linear analog weight update are demonstrated. Simulations using the handwritten digit data sets demonstrate high recognition accuracy (94.1%) of the synaptic transistor arrays. These results provide an insight into the application of 2D oxides for large-scale, energy-efficient neuromorphic computing networks.
- Authors:
-
[1];
- Chinese Academy of Sciences (CAS), Beijing (China). Inst. of Physics. Beijing National Lab. for Condensed Matter Physics (BNLCP-CAS)
- Sandia National Lab. (SNL-CA), Livermore, CA (United States)
- Publication Date:
- Research Org.:
- Energy Frontier Research Centers (EFRC) (United States). Nanostructures for Electrical Energy Storage (NEES); Sandia National Lab. (SNL-CA), Livermore, CA (United States)
- Sponsoring Org.:
- USDOE National Nuclear Security Administration (NNSA); USDOE Office of Science (SC), Basic Energy Sciences (BES); National Nature Science Foundation of China (NSFC); National Key Research Program of China; Chinese Academy of Sciences (CAS); Univ. of Maryland, College Park, MD (United States). Nanostructures for Electrical Energy Storage (NEES)
- OSTI Identifier:
- 1472248
- Alternate Identifier(s):
- OSTI ID: 1468672
- Report Number(s):
- SAND-2018-9254J
Journal ID: ISSN 1616-301X; 667286
- Grant/Contract Number:
- AC04-94AL85000; 61874138; 51671213; 11534015; 51725104; 2016YFA0300701; XDB07030200; P2018‐004; SC0001160; NA0003525; NA‐0003525
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Advanced Functional Materials
- Additional Journal Information:
- Journal Volume: 28; Journal Issue: 42; Journal ID: ISSN 1616-301X
- Publisher:
- Wiley
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 97 MATHEMATICS AND COMPUTING; 75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND SUPERFLUIDITY; electrochemical transistor; ion intercalation; molybdenum oxide; synaptic plasticity; synaptic transistor
Citation Formats
Yang, Chuan-Sen, Shang, Da-Shan, Liu, Nan, Fuller, Elliot J., Agrawal, Sapan, Talin, Albert Alec, Li, Yong-Qing, Shen, Bao-Gen, and Sun, Young. All-Solid-State Synaptic Transistor with Ultralow Conductance for Neuromorphic Computing. United States: N. p., 2018.
Web. doi:10.1002/adfm.201804170.
Yang, Chuan-Sen, Shang, Da-Shan, Liu, Nan, Fuller, Elliot J., Agrawal, Sapan, Talin, Albert Alec, Li, Yong-Qing, Shen, Bao-Gen, & Sun, Young. All-Solid-State Synaptic Transistor with Ultralow Conductance for Neuromorphic Computing. United States. https://doi.org/10.1002/adfm.201804170
Yang, Chuan-Sen, Shang, Da-Shan, Liu, Nan, Fuller, Elliot J., Agrawal, Sapan, Talin, Albert Alec, Li, Yong-Qing, Shen, Bao-Gen, and Sun, Young. Wed .
"All-Solid-State Synaptic Transistor with Ultralow Conductance for Neuromorphic Computing". United States. https://doi.org/10.1002/adfm.201804170. https://www.osti.gov/servlets/purl/1472248.
@article{osti_1472248,
title = {All-Solid-State Synaptic Transistor with Ultralow Conductance for Neuromorphic Computing},
author = {Yang, Chuan-Sen and Shang, Da-Shan and Liu, Nan and Fuller, Elliot J. and Agrawal, Sapan and Talin, Albert Alec and Li, Yong-Qing and Shen, Bao-Gen and Sun, Young},
abstractNote = {Electronic synaptic devices are important building blocks for neuromorphic computational systems that can go beyond the constraints of von Neumann architecture. Although two-terminal memristive devices are demonstrated to be possible candidates, they suffer from several shortcomings related to the filament formation mechanism including nonlinear switching, write noise, and high device conductance, all of which limit the accuracy and energy efficiency. Electrochemical three-terminal transistors, in which the channel conductance can be tuned without filament formation provide an alternative platform for synaptic electronics. In this work, an all-solid-state electrochemical transistor made with Li ion–based solid dielectric and 2D α-phase molybdenum oxide (α-MoO3) nanosheets as the channel is demonstrated. These devices achieve nonvolatile conductance modulation in an ultralow conductance regime (<75 nS) by reversible intercalation of Li ions into the α-MoO3 lattice. Based on this operating mechanism, the essential functionalities of synapses, such as short- and long-term synaptic plasticity and bidirectional near-linear analog weight update are demonstrated. Simulations using the handwritten digit data sets demonstrate high recognition accuracy (94.1%) of the synaptic transistor arrays. These results provide an insight into the application of 2D oxides for large-scale, energy-efficient neuromorphic computing networks.},
doi = {10.1002/adfm.201804170},
journal = {Advanced Functional Materials},
number = 42,
volume = 28,
place = {United States},
year = {Wed Sep 05 00:00:00 EDT 2018},
month = {Wed Sep 05 00:00:00 EDT 2018}
}
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