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A Compact Model for 2-D Poly-MoS2 FETs With Resistive Switching in Postsynaptic Simulation.

Authors :
Wang, Lingfei
Wang, Lin
Ang, Kah-Wee
Thean, Aaron Voon-Yew
Liang, Gengchiau
Source :
IEEE Transactions on Electron Devices. Sep2019, Vol. 66 Issue 9, p4092-4100. 9p.
Publication Year :
2019

Abstract

The analog resistive switching (RS) characteristics in 2-D polycrystalline (poly-) molybdenum disulfide (MoS2) field-effect transistors (FETs) enable new electronic devices capable of emulating biological synaptic behaviors. In 2-D poly-materials, grain boundary (GB)-induced trap states are of major significance to RS behaviors. However, there is still a lack of appropriate compact models that capture accurate physical mechanisms. Therefore, we developed a surface potential-based compact model, based on the theories of the GB energy barrier and space charge limited current (SCLC). By calibrating to experimental data of MoS2, the physical parameters are extracted, and the model explains the scaling behaviors of channel lengths and grain sizes. Due to the electric-field-induced defect redistribution, the energy barrier modulation at a single-GB (e.g., intersecting GB) quantitatively matches the reported experiments. Moreover, the possible SCLC-based RS behavior is also investigated. Furthermore, we have optimized the set/reset process and simulated the postsynaptic current (PSC) with a tunable potentiation (or depression) process, and then the gate voltage dependence and statistical effects on RS and PSC have been investigated. Thus, this model provides important devices physics insights of 2-D poly-materials and it guides device design, fabrication, and material engineering, to meet the requirements of the future neuromorphic computing application. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
00189383
Volume :
66
Issue :
9
Database :
Academic Search Index
Journal :
IEEE Transactions on Electron Devices
Publication Type :
Academic Journal
Accession number :
138938185
Full Text :
https://doi.org/10.1109/TED.2019.2931069