All‐Solid‐State Synaptic Transistor with Ultralow Conductance for Neuromorphic Computing

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 f...

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Published in:	Advanced functional materials Vol. 28; no. 42
Main Authors:	Yang, Chuan‐Sen, Shang, Da‐Shan, Liu, Nan, Fuller, Elliot J., Agrawal, Sapan, Talin, A. Alec, Li, Yong‐Qing, Shen, Bao‐Gen, Sun, Young
Format:	Journal Article
Language:	English
Published:	Hoboken Wiley Subscription Services, Inc 17.10.2018 Wiley
Subjects:	Computer simulation CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND SUPERFLUIDITY electrochemical transistor Electronic devices Handwriting Handwriting recognition ion intercalation Materials science MATHEMATICS AND COMPUTING molybdenum oxide Molybdenum oxides Molybdenum trioxide Neuromorphic computing Resistance Scale (corrosion) Semiconductor devices Switching theory Synapses synaptic plasticity synaptic transistor Transistors Weight
ISSN:	1616-301X, 1616-3028
Online Access:	Get full text
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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. Here, 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. All‐solid‐state synaptic transistors based on 2D α‐MoO3 nanosheets are fabricated. The operation mechanism is based on the gate voltage–induced reversible intercalation of Li‐ion dopants into α‐MoO3 channel lattice, which engenders bidirectional, near‐linear analog modulation of channel conductance in an ultralow conductance regime (<75 nS). The essential functionalities of synapses and neuromorphic computing for image recognition are demonstrated.
AbstractList	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. Here, an all‐solid‐state electrochemical transistor made with Li ion–based solid dielectric and 2D α‐phase molybdenum oxide (α‐MoO 3 ) 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‐MoO 3 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. 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. Here, 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. 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. 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. Here, 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. All‐solid‐state synaptic transistors based on 2D α‐MoO3 nanosheets are fabricated. The operation mechanism is based on the gate voltage–induced reversible intercalation of Li‐ion dopants into α‐MoO3 channel lattice, which engenders bidirectional, near‐linear analog modulation of channel conductance in an ultralow conductance regime (<75 nS). The essential functionalities of synapses and neuromorphic computing for image recognition are demonstrated.
Author	Fuller, Elliot J. Li, Yong‐Qing Liu, Nan Agrawal, Sapan Shen, Bao‐Gen Sun, Young Talin, A. Alec Yang, Chuan‐Sen Shang, Da‐Shan
Author_xml	– sequence: 1 givenname: Chuan‐Sen surname: Yang fullname: Yang, Chuan‐Sen organization: Chinese Academy of Sciences – sequence: 2 givenname: Da‐Shan orcidid: 0000-0003-3573-8390 surname: Shang fullname: Shang, Da‐Shan email: shangdashan@iphy.ac.cn organization: Chinese Academy of Sciences – sequence: 3 givenname: Nan surname: Liu fullname: Liu, Nan organization: Chinese Academy of Sciences – sequence: 4 givenname: Elliot J. surname: Fuller fullname: Fuller, Elliot J. organization: Sandia National Laboratories – sequence: 5 givenname: Sapan surname: Agrawal fullname: Agrawal, Sapan organization: Sandia National Laboratories – sequence: 6 givenname: A. Alec surname: Talin fullname: Talin, A. Alec organization: Sandia National Laboratories – sequence: 7 givenname: Yong‐Qing surname: Li fullname: Li, Yong‐Qing organization: Chinese Academy of Sciences – sequence: 8 givenname: Bao‐Gen surname: Shen fullname: Shen, Bao‐Gen organization: Chinese Academy of Sciences – sequence: 9 givenname: Young surname: Sun fullname: Sun, Young email: youngsun@iphy.ac.cn organization: Chinese Academy of Sciences
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Snippet	Electronic synaptic devices are important building blocks for neuromorphic computational systems that can go beyond the constraints of von Neumann...
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SubjectTerms	Computer simulation CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND SUPERFLUIDITY electrochemical transistor Electronic devices Handwriting Handwriting recognition ion intercalation Materials science MATHEMATICS AND COMPUTING molybdenum oxide Molybdenum oxides Molybdenum trioxide Neuromorphic computing Resistance Scale (corrosion) Semiconductor devices Switching theory Synapses synaptic plasticity synaptic transistor Transistors Weight
Title	All‐Solid‐State Synaptic Transistor with Ultralow Conductance for Neuromorphic Computing
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