Assessment of trapping layer control in IGZO/Al2O3/Ga2O3 synaptic transistor for neuromorphic computing

被引:9
作者
Jo, Eun Seo [2 ,3 ]
Rim, You Seung [1 ,2 ,3 ]
机构
[1] Sejong Univ, Dept Intelligent Mechatron Engn & Convergence Engn, Seoul 05006, South Korea
[2] Sejong Univ, Dept Semicond Syst Engn, Seoul 05006, South Korea
[3] Sejong Univ, Inst Semicond & Syst IC, Seoul 05006, South Korea
基金
新加坡国家研究基金会;
关键词
Neuromorphic; Synaptic transistor; Charge trap memory; IGZO; FIELD-EFFECT TRANSISTOR; INTERFACE-TRAP;
D O I
10.1016/j.mtphys.2023.101194
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
We conducted research to create reverse synapse plasticity using metal oxide semiconductor-based field-effect transistors. Specifically, we used IGZO as the channel, Al2O3 as the tunneling layer, and Ga2O3 as the trapping layer. We adjusted the thickness of the Ga2O3 trapping layers and examined changes in roughness and density. Through this examination, we confirmed the variations and mechanisms of synaptic behaviors with respect to the properties of Ga2O3. We found that controlling the charge traps as functions of pulse time, input voltage, and initialization is key to approaching optimal device conditions. As a result of our research, we obtained a maximum nonlinearity factor of & nu; = 0.27 for G of synaptic plasticity. This high degree of linearity, particularly near zero nonlinearity, is significant for neuromorphic research in pattern recognition.
引用
收藏
页数:7
相关论文
共 56 条
[1]   Conductive Bridge Random Access Memory (CBRAM): Challenges and Opportunities for Memory and Neuromorphic Computing Applications [J].
Abbas, Haider ;
Li, Jiayi ;
Ang, Diing Shenp .
MICROMACHINES, 2022, 13 (05)
[2]   Energy-Efficient III-V Tunnel FET-Based Synaptic Device with Enhanced Charge Trapping Ability Utilizing Both Hot Hole and Hot Electron Injections for Analog Neuromorphic Computing [J].
Ahn, Dae-Hwan ;
Hu, Suman ;
Ko, Kyeol ;
Park, Donghee ;
Suh, Hoyoung ;
Kim, Gyu-Tae ;
Han, Jae-Hoon ;
Song, Jin-Dong ;
Jeong, YeonJoo .
ACS APPLIED MATERIALS & INTERFACES, 2022, 14 (21) :24592-24601
[3]  
[Anonymous], 2023, Short-Term Memory Characteristics of IGZO-Based Three-Terminal Devices, P1
[4]   Neuromorphic computing using non-volatile memory [J].
Burr, Geoffrey W. ;
Shelby, Robert M. ;
Sebastian, Abu ;
Kim, Sangbum ;
Kim, Seyoung ;
Sidler, Severin ;
Virwani, Kumar ;
Ishii, Masatoshi ;
Narayanan, Pritish ;
Fumarola, Alessandro ;
Sanches, Lucas L. ;
Boybat, Irem ;
Le Gallo, Manuel ;
Moon, Kibong ;
Woo, Jiyoo ;
Hwang, Hyunsang ;
Leblebici, Yusuf .
ADVANCES IN PHYSICS-X, 2017, 2 (01) :89-124
[5]   2D Material Based Synaptic Devices for Neuromorphic Computing [J].
Cao, Guiming ;
Meng, Peng ;
Chen, Jiangang ;
Liu, Haishi ;
Bian, Renji ;
Zhu, Chao ;
Liu, Fucai ;
Liu, Zheng .
ADVANCED FUNCTIONAL MATERIALS, 2021, 31 (04)
[6]   A spiking neuron circuit based on a carbon nanotube transistor [J].
Chen, C-L ;
Kim, K. ;
Truong, Q. ;
Shen, A. ;
Li, Z. ;
Chen, Y. .
NANOTECHNOLOGY, 2012, 23 (27)
[7]   Vertical organic synapse expandable to 3D crossbar array [J].
Choi, Yongsuk ;
Oh, Seyong ;
Qian, Chuan ;
Park, Jin-Hong ;
Cho, Jeong Ho .
NATURE COMMUNICATIONS, 2020, 11 (01)
[8]   Recent Advances in Transistor-Based Artificial Synapses [J].
Dai, Shilei ;
Zhao, Yiwei ;
Wang, Yan ;
Zhang, Junyao ;
Fang, Lu ;
Jin, Shu ;
Shao, Yinlin ;
Huang, Jia .
ADVANCED FUNCTIONAL MATERIALS, 2019, 29 (42)
[9]   Liquid-Gated Organic Electronic Devices Based on High-Performance Solution-Processed Molecular Semiconductor [J].
Di Lauro, Michele ;
Berto, Marcello ;
Giordani, Martina ;
Benaglia, Simone ;
Schweicher, Guillaume ;
Vuillaume, Dominique ;
Bortolotti, Carlo A. ;
Geerts, Yves H. ;
Biscarini, Fabio .
ADVANCED ELECTRONIC MATERIALS, 2017, 3 (09)
[10]   Reservoir Computing with Charge-Trap Memory Based on a MoS2 Channel for Neuromorphic Engineering [J].
Farronato, Matteo ;
Mannocci, Piergiulio ;
Melegari, Margherita ;
Ricci, Saverio ;
Compagnoni, Christian Monzio ;
Ielmini, Daniele .
ADVANCED MATERIALS, 2023, 35 (37)