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Hysteresis Modulation on Van der Waals‐Based Ferroelectric Field‐Effect Transistor by Interfacial Passivation Technique and Its Application in Optic Neural Networks
Author(s) -
Jeon Hyeok,
Kim SeungGeun,
Park June,
Kim SeungHwan,
Park Euyjin,
Kim Jiyoung,
Yu HyunYong
Publication year - 2020
Publication title -
small
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.785
H-Index - 236
eISSN - 1613-6829
pISSN - 1613-6810
DOI - 10.1002/smll.202004371
Subject(s) - materials science , passivation , ferroelectricity , neuromorphic engineering , optoelectronics , hysteresis , field effect transistor , transistor , semiconductor , field effect , electronic engineering , nanotechnology , artificial neural network , voltage , computer science , electrical engineering , condensed matter physics , layer (electronics) , physics , dielectric , engineering , machine learning
2D semiconductor‐based ferroelectric field effect transistors (FeFETs) have been considered as a promising artificial synaptic device for implementation of neuromorphic computing systems. However, an inevitable problem, interface traps at the 2D semiconductor/ferroelectric oxide interface, suppresses ferroelectric characteristics, and causes a critical degradation on the performance of 2D‐based FeFETs. Here, hysteresis modulation method using self‐assembly monolayer (SAM) material for interface trap passivation on 2D‐based FeFET is presented. Through effectively passivation of interface traps by SAM layer, the hysteresis of the proposed device changes from interface traps‐dependent to polarization‐dependent direction. The reduction of interface trap density is clearly confirmed through the result of calculation using the subthreshold swing of the device. Furthermore, excellent optic‐neural synaptic characteristics are successfully implemeted, including linear and symmetric potentiation and depression, and multilevel conductance. This work identifies the potential of passivation effect for 2D‐based FeFETs to accelerate the development of neuromorphic computing systems.