Metal-Oxide Thin-Film Transistors for Artificial Neural Networks

Yushen Hu; Tengteng Lei; Man Wong

doi:10.1109/ICSICT62049.2024.10831265

Metal-Oxide Thin-Film Transistors for Artificial Neural Networks

Yushen Hu
, Tengteng Lei
, Man Wong

Department of Electronic and Computer Engineering

Research output: Chapter in Book/Conference Proceeding/Report › Conference Paper published in a book › peer-review

Abstract

The technology of thin-film transistors (TFTs) enables the construction of both single- and dual-gate devices. When built on metal oxide (MO) semiconductors with large energy bandgaps, TFTs with exceptionally low leakage current can be realized. This combination of materials, structures and electrical characteristics enables the deployment of MO TFTs in a wide range of applications. Presently reviewed are the deployment of a dual-gate TFT as a biomimetic electronic synapse and its application to the construction of artificial neural networks (ANNs).

Original language	English
Title of host publication	2024 IEEE 17th International Conference on Solid-State and Integrated Circuit Technology, ICSICT 2024
Editors	Fan Ye, Xiaona Zhu, Ting Ao Tang
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9798350361834
DOIs	https://doi.org/10.1109/ICSICT62049.2024.10831265
Publication status	Published - 2024
Event	17th IEEE International Conference on Solid-State and Integrated Circuit Technology, ICSICT 2024 - Zhuhai, China Duration: 22 Oct 2024 → 25 Oct 2024

Publication series

Name	2024 IEEE 17th International Conference on Solid-State and Integrated Circuit Technology, ICSICT 2024

Conference

Conference	17th IEEE International Conference on Solid-State and Integrated Circuit Technology, ICSICT 2024
Country/Territory	China
City	Zhuhai
Period	22/10/24 → 25/10/24

Bibliographical note

Publisher Copyright:
© 2024 IEEE.

Keywords

Thin-film transistor (TFT)
artificial neural network (ANN)
dual-gate (DG)
metal oxide (MO) semiconductor
spiking neural network (SNN)

Access to Document

10.1109/ICSICT62049.2024.10831265

Cite this

Hu, Y., Lei, T., & Wong, M. (2024). Metal-Oxide Thin-Film Transistors for Artificial Neural Networks. In F. Ye, X. Zhu, & T. A. Tang (Eds.), 2024 IEEE 17th International Conference on Solid-State and Integrated Circuit Technology, ICSICT 2024 (2024 IEEE 17th International Conference on Solid-State and Integrated Circuit Technology, ICSICT 2024). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICSICT62049.2024.10831265

Hu, Yushen ; Lei, Tengteng ; Wong, Man. / Metal-Oxide Thin-Film Transistors for Artificial Neural Networks. 2024 IEEE 17th International Conference on Solid-State and Integrated Circuit Technology, ICSICT 2024. editor / Fan Ye ; Xiaona Zhu ; Ting Ao Tang. Institute of Electrical and Electronics Engineers Inc., 2024. (2024 IEEE 17th International Conference on Solid-State and Integrated Circuit Technology, ICSICT 2024).

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title = "Metal-Oxide Thin-Film Transistors for Artificial Neural Networks",

abstract = "The technology of thin-film transistors (TFTs) enables the construction of both single- and dual-gate devices. When built on metal oxide (MO) semiconductors with large energy bandgaps, TFTs with exceptionally low leakage current can be realized. This combination of materials, structures and electrical characteristics enables the deployment of MO TFTs in a wide range of applications. Presently reviewed are the deployment of a dual-gate TFT as a biomimetic electronic synapse and its application to the construction of artificial neural networks (ANNs).",

keywords = "Thin-film transistor (TFT), artificial neural network (ANN), dual-gate (DG), metal oxide (MO) semiconductor, spiking neural network (SNN)",

author = "Yushen Hu and Tengteng Lei and Man Wong",

note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 17th IEEE International Conference on Solid-State and Integrated Circuit Technology, ICSICT 2024 ; Conference date: 22-10-2024 Through 25-10-2024",

year = "2024",

doi = "10.1109/ICSICT62049.2024.10831265",

language = "English",

series = "2024 IEEE 17th International Conference on Solid-State and Integrated Circuit Technology, ICSICT 2024",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

editor = "Fan Ye and Xiaona Zhu and Tang, \{Ting Ao\}",

booktitle = "2024 IEEE 17th International Conference on Solid-State and Integrated Circuit Technology, ICSICT 2024",

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Hu, Y, Lei, T & Wong, M 2024, Metal-Oxide Thin-Film Transistors for Artificial Neural Networks. in F Ye, X Zhu & TA Tang (eds), 2024 IEEE 17th International Conference on Solid-State and Integrated Circuit Technology, ICSICT 2024. 2024 IEEE 17th International Conference on Solid-State and Integrated Circuit Technology, ICSICT 2024, Institute of Electrical and Electronics Engineers Inc., 17th IEEE International Conference on Solid-State and Integrated Circuit Technology, ICSICT 2024, Zhuhai, China, 22/10/24. https://doi.org/10.1109/ICSICT62049.2024.10831265

Metal-Oxide Thin-Film Transistors for Artificial Neural Networks. / Hu, Yushen; Lei, Tengteng; Wong, Man.
2024 IEEE 17th International Conference on Solid-State and Integrated Circuit Technology, ICSICT 2024. ed. / Fan Ye; Xiaona Zhu; Ting Ao Tang. Institute of Electrical and Electronics Engineers Inc., 2024. (2024 IEEE 17th International Conference on Solid-State and Integrated Circuit Technology, ICSICT 2024).

Research output: Chapter in Book/Conference Proceeding/Report › Conference Paper published in a book › peer-review

TY - GEN

T1 - Metal-Oxide Thin-Film Transistors for Artificial Neural Networks

AU - Hu, Yushen

AU - Lei, Tengteng

AU - Wong, Man

PY - 2024

Y1 - 2024

N2 - The technology of thin-film transistors (TFTs) enables the construction of both single- and dual-gate devices. When built on metal oxide (MO) semiconductors with large energy bandgaps, TFTs with exceptionally low leakage current can be realized. This combination of materials, structures and electrical characteristics enables the deployment of MO TFTs in a wide range of applications. Presently reviewed are the deployment of a dual-gate TFT as a biomimetic electronic synapse and its application to the construction of artificial neural networks (ANNs).

AB - The technology of thin-film transistors (TFTs) enables the construction of both single- and dual-gate devices. When built on metal oxide (MO) semiconductors with large energy bandgaps, TFTs with exceptionally low leakage current can be realized. This combination of materials, structures and electrical characteristics enables the deployment of MO TFTs in a wide range of applications. Presently reviewed are the deployment of a dual-gate TFT as a biomimetic electronic synapse and its application to the construction of artificial neural networks (ANNs).

KW - Thin-film transistor (TFT)

KW - artificial neural network (ANN)

KW - dual-gate (DG)

KW - metal oxide (MO) semiconductor

KW - spiking neural network (SNN)

UR - https://www.scopus.com/pages/publications/85218125358

U2 - 10.1109/ICSICT62049.2024.10831265

DO - 10.1109/ICSICT62049.2024.10831265

M3 - Conference Paper published in a book

AN - SCOPUS:85218125358

T3 - 2024 IEEE 17th International Conference on Solid-State and Integrated Circuit Technology, ICSICT 2024

BT - 2024 IEEE 17th International Conference on Solid-State and Integrated Circuit Technology, ICSICT 2024

A2 - Ye, Fan

A2 - Zhu, Xiaona

A2 - Tang, Ting Ao

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 17th IEEE International Conference on Solid-State and Integrated Circuit Technology, ICSICT 2024

Y2 - 22 October 2024 through 25 October 2024

ER -

Hu Y, Lei T, Wong M. Metal-Oxide Thin-Film Transistors for Artificial Neural Networks. In Ye F, Zhu X, Tang TA, editors, 2024 IEEE 17th International Conference on Solid-State and Integrated Circuit Technology, ICSICT 2024. Institute of Electrical and Electronics Engineers Inc. 2024. (2024 IEEE 17th International Conference on Solid-State and Integrated Circuit Technology, ICSICT 2024). doi: 10.1109/ICSICT62049.2024.10831265

Metal-Oxide Thin-Film Transistors for Artificial Neural Networks

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