Electrospun Nanofiber-Based Synaptic Transistor with Tunable Plasticity for Neuromorphic Computing

Yizhe Guo; Fan Wu; Guan-Hua Dun; Tianrui Cui; Yanming Liu; Xichao Tan; Yancong Qiao; Mario Lanza; He Tian; Yi Yang; Tian-Ling Ren

doi:10.1002/adfm.202208055

Electrospun Nanofiber-Based Synaptic Transistor with Tunable Plasticity for Neuromorphic Computing

Yizhe Guo, Fan Wu, Guan-Hua Dun, Tianrui Cui, Yanming Liu, Xichao Tan, Yancong Qiao, Mario Lanza, He Tian, Yi Yang, Tian-Ling Ren

Research output: Contribution to journal › Article › peer-review

13 Scopus citations

Abstract

Biological synapses are the operational connection of the neurons for signal transmission in neuromorphic networks and hardware implementation combined with electrospun 1D nanofibers have realized its functionality for complicated computing tasks in basic three-terminal field-effect transistors with gate-controlled channel conductance. However, it still lacks the fundamental understanding that how the technological parameters influence the signal intensity of the information processing in the neural systems for the nanofiber-based synaptic transistors. Here, by tuning the electrospinning parameters and introducing the channel surface doping, an electrospun ZnO nanofiber-based transistor with tunable plasticity is presented to emulate the changing synaptic functions. The underlying mechanism of influence of carrier concentration and mobility on the device's electrical and synaptic performance is revealed as well. Short-term plasticity behaviors including paired-pulse facilitation, spike duration-dependent plasticity, and dynamic filtering are tuned in this fiber-based device. Furthermore, Perovskite-doped devices with ultralow energy consumption down to ≈0.2554 fJ and their handwritten recognition application show the great potential of synaptic transistors based on a 1D nanostructure active layer for building next-generation neuromorphic networks.

Original language	English (US)
Pages (from-to)	2208055
Journal	Advanced Functional Materials
DOIs	https://doi.org/10.1002/adfm.202208055
State	Published - Dec 12 2022

Bibliographical note

KAUST Repository Item: Exported on 2022-12-15
Acknowledgements: This work was supported in part by the National Natural Science Foundation of China under Grant 62022047, Grant 61874065, Grant U20A20168 and Grant 51861145202, in part by the National Key R&D Program under Grant 2022YFB3204100, Grant 2021YFC3002200 and Grant 2020YFA0709800, in part by Science and Technology Innovation 2030-‘New Generation Artificial Intelligence’ Major Project under Grant 2022ZD0209200, in part by Fok Ying-Tong Education Foundation under Grant 171051, in part by Beijing Natural Science Foundation (M22020), in part by Beijing National Research Center for Information Science and Technology Youth Innovation Fund (BNR2021RC01007) and in part by State Key Laboratory of New Ceramic and Fine Processing Tsinghua University (No. KF202109), in part by Tsinghua-Foshan Innovation Special Fund(TFISF) (2021THFS0217), in part by the Research Fund from Beijing Innovation Center for Future Chip, in part by Tsinghua-Toyota Joint Research Fund, the Independent Research Program of Tsinghua University under Grant 20193080047 and supported by the Opening Project of Key Laboratory of Microelectronic Devices & Integrated Technology, Institute of Microelectronics, Chinese Academy of Sciences.

ASJC Scopus subject areas

Biomaterials
Electrochemistry
Electronic, Optical and Magnetic Materials
Condensed Matter Physics

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1002/adfm.202208055

Cite this

@article{55c2aa5eba3d42af8370a034b1adbc74,

title = "Electrospun Nanofiber-Based Synaptic Transistor with Tunable Plasticity for Neuromorphic Computing",

abstract = "Biological synapses are the operational connection of the neurons for signal transmission in neuromorphic networks and hardware implementation combined with electrospun 1D nanofibers have realized its functionality for complicated computing tasks in basic three-terminal field-effect transistors with gate-controlled channel conductance. However, it still lacks the fundamental understanding that how the technological parameters influence the signal intensity of the information processing in the neural systems for the nanofiber-based synaptic transistors. Here, by tuning the electrospinning parameters and introducing the channel surface doping, an electrospun ZnO nanofiber-based transistor with tunable plasticity is presented to emulate the changing synaptic functions. The underlying mechanism of influence of carrier concentration and mobility on the device's electrical and synaptic performance is revealed as well. Short-term plasticity behaviors including paired-pulse facilitation, spike duration-dependent plasticity, and dynamic filtering are tuned in this fiber-based device. Furthermore, Perovskite-doped devices with ultralow energy consumption down to ≈0.2554 fJ and their handwritten recognition application show the great potential of synaptic transistors based on a 1D nanostructure active layer for building next-generation neuromorphic networks.",

author = "Yizhe Guo and Fan Wu and Guan-Hua Dun and Tianrui Cui and Yanming Liu and Xichao Tan and Yancong Qiao and Mario Lanza and He Tian and Yi Yang and Tian-Ling Ren",

note = "KAUST Repository Item: Exported on 2022-12-15 Acknowledgements: This work was supported in part by the National Natural Science Foundation of China under Grant 62022047, Grant 61874065, Grant U20A20168 and Grant 51861145202, in part by the National Key R&D Program under Grant 2022YFB3204100, Grant 2021YFC3002200 and Grant 2020YFA0709800, in part by Science and Technology Innovation 2030-{\textquoteleft}New Generation Artificial Intelligence{\textquoteright} Major Project under Grant 2022ZD0209200, in part by Fok Ying-Tong Education Foundation under Grant 171051, in part by Beijing Natural Science Foundation (M22020), in part by Beijing National Research Center for Information Science and Technology Youth Innovation Fund (BNR2021RC01007) and in part by State Key Laboratory of New Ceramic and Fine Processing Tsinghua University (No. KF202109), in part by Tsinghua-Foshan Innovation Special Fund(TFISF) (2021THFS0217), in part by the Research Fund from Beijing Innovation Center for Future Chip, in part by Tsinghua-Toyota Joint Research Fund, the Independent Research Program of Tsinghua University under Grant 20193080047 and supported by the Opening Project of Key Laboratory of Microelectronic Devices & Integrated Technology, Institute of Microelectronics, Chinese Academy of Sciences.",

year = "2022",

month = dec,

day = "12",

doi = "10.1002/adfm.202208055",

language = "English (US)",

pages = "2208055",

journal = "Advanced Functional Materials",

issn = "1616-301X",

publisher = "John Wiley and Sons Ltd",

}

TY - JOUR

T1 - Electrospun Nanofiber-Based Synaptic Transistor with Tunable Plasticity for Neuromorphic Computing

AU - Guo, Yizhe

AU - Wu, Fan

AU - Dun, Guan-Hua

AU - Cui, Tianrui

AU - Liu, Yanming

AU - Tan, Xichao

AU - Qiao, Yancong

AU - Lanza, Mario

AU - Tian, He

AU - Yang, Yi

AU - Ren, Tian-Ling

N1 - KAUST Repository Item: Exported on 2022-12-15 Acknowledgements: This work was supported in part by the National Natural Science Foundation of China under Grant 62022047, Grant 61874065, Grant U20A20168 and Grant 51861145202, in part by the National Key R&D Program under Grant 2022YFB3204100, Grant 2021YFC3002200 and Grant 2020YFA0709800, in part by Science and Technology Innovation 2030-‘New Generation Artificial Intelligence’ Major Project under Grant 2022ZD0209200, in part by Fok Ying-Tong Education Foundation under Grant 171051, in part by Beijing Natural Science Foundation (M22020), in part by Beijing National Research Center for Information Science and Technology Youth Innovation Fund (BNR2021RC01007) and in part by State Key Laboratory of New Ceramic and Fine Processing Tsinghua University (No. KF202109), in part by Tsinghua-Foshan Innovation Special Fund(TFISF) (2021THFS0217), in part by the Research Fund from Beijing Innovation Center for Future Chip, in part by Tsinghua-Toyota Joint Research Fund, the Independent Research Program of Tsinghua University under Grant 20193080047 and supported by the Opening Project of Key Laboratory of Microelectronic Devices & Integrated Technology, Institute of Microelectronics, Chinese Academy of Sciences.

PY - 2022/12/12

Y1 - 2022/12/12

N2 - Biological synapses are the operational connection of the neurons for signal transmission in neuromorphic networks and hardware implementation combined with electrospun 1D nanofibers have realized its functionality for complicated computing tasks in basic three-terminal field-effect transistors with gate-controlled channel conductance. However, it still lacks the fundamental understanding that how the technological parameters influence the signal intensity of the information processing in the neural systems for the nanofiber-based synaptic transistors. Here, by tuning the electrospinning parameters and introducing the channel surface doping, an electrospun ZnO nanofiber-based transistor with tunable plasticity is presented to emulate the changing synaptic functions. The underlying mechanism of influence of carrier concentration and mobility on the device's electrical and synaptic performance is revealed as well. Short-term plasticity behaviors including paired-pulse facilitation, spike duration-dependent plasticity, and dynamic filtering are tuned in this fiber-based device. Furthermore, Perovskite-doped devices with ultralow energy consumption down to ≈0.2554 fJ and their handwritten recognition application show the great potential of synaptic transistors based on a 1D nanostructure active layer for building next-generation neuromorphic networks.

AB - Biological synapses are the operational connection of the neurons for signal transmission in neuromorphic networks and hardware implementation combined with electrospun 1D nanofibers have realized its functionality for complicated computing tasks in basic three-terminal field-effect transistors with gate-controlled channel conductance. However, it still lacks the fundamental understanding that how the technological parameters influence the signal intensity of the information processing in the neural systems for the nanofiber-based synaptic transistors. Here, by tuning the electrospinning parameters and introducing the channel surface doping, an electrospun ZnO nanofiber-based transistor with tunable plasticity is presented to emulate the changing synaptic functions. The underlying mechanism of influence of carrier concentration and mobility on the device's electrical and synaptic performance is revealed as well. Short-term plasticity behaviors including paired-pulse facilitation, spike duration-dependent plasticity, and dynamic filtering are tuned in this fiber-based device. Furthermore, Perovskite-doped devices with ultralow energy consumption down to ≈0.2554 fJ and their handwritten recognition application show the great potential of synaptic transistors based on a 1D nanostructure active layer for building next-generation neuromorphic networks.

UR - http://hdl.handle.net/10754/686431

UR - https://onlinelibrary.wiley.com/doi/10.1002/adfm.202208055

U2 - 10.1002/adfm.202208055

DO - 10.1002/adfm.202208055

M3 - Article

SN - 1616-301X

SP - 2208055

JO - Advanced Functional Materials

JF - Advanced Functional Materials

ER -

Electrospun Nanofiber-Based Synaptic Transistor with Tunable Plasticity for Neuromorphic Computing

Abstract

Bibliographical note

ASJC Scopus subject areas

UN SDGs

Access to Document

Other files and links

Fingerprint

Cite this