4.8 Article

Hysteresis Modulation on Van der Waals-Based Ferroelectric Field-Effect Transistor by Interfacial Passivation Technique and Its Application in Optic Neural Networks

Journal

SMALL
Volume 16, Issue 49, Pages -

Publisher

WILEY-V C H VERLAG GMBH
DOI: 10.1002/smll.202004371

Keywords

2D‐ based ferroelectric field effect transistors; HfO2‐ based ferroelectric materials; optic‐ neural synapses; passivation effects; 2D materials

Funding

  1. National Research Foundation of Korea(NRF) - Ministry of Science and ICT for Original Technology Program [2020M3F3A2A01082329]
  2. Basic Science Research Program within the Ministry of Science, ICT, and Future Planning through the National Research Foundation of Korea [2020R1A2C2004029]
  3. National Research Foundation of Korea [4199990114242] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)

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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.

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