Hybrid Modeling and Sensitivity Analysis on Reduced Graphene Oxide Field-Effect Transistor

Chao Wang; Haihui Pu; Xiaoyu Sui; Shiyu Zhou; Junhong Chen

doi:10.1109/TNANO.2021.3076135

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Hybrid Modeling and Sensitivity Analysis on Reduced Graphene Oxide Field-Effect Transistor

Journal article

Peer reviewed

Hybrid Modeling and Sensitivity Analysis on Reduced Graphene Oxide Field-Effect Transistor

Chao Wang, Haihui Pu, Xiaoyu Sui, Shiyu Zhou and Junhong Chen

IEEE transactions on nanotechnology, Vol.20, pp.404-416

2021

DOI: 10.1109/TNANO.2021.3076135

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Abstract

The reduced graphene oxide (RGO) field-effect transistor (FET) has been developed and applied in various areas. However, the effective modeling and sensitivity analysis on RGO FET is still a very challenging problem due to the randomness of bandgap and density of states (DOS) in RGO. In this paper, we propose to solve the RGO FET modeling problem by integrating the data-driven thinking and the graphene FET model to develop a hybrid model. The proposed model takes advantages of the similarities between graphene and RGO to generalize the existing graphene FET model, and employs RGO FET drain-current data to characterize the specificity of the model. The basic idea in the proposed model is to modify the graphene DOS to approximate the RGO DOS so that the charge density, mobility and other parameters can be achieved through the approximated RGO DOS. We validate the model accuracy with the RGO FET based sensors that detect chemical concentrations in the aqueous environment. The RGO FET sensitivity analysis is also demonstrated to provide guidance for RGO FET application and manufacturing.

Analytical models

Biological system modeling

compact model

Data models

Field effect transistors

field-effect transistor

Graphene

Photonic band gap

Reduced graphene oxide

Sensitivity analysis

Details

Title: Subtitle: Hybrid Modeling and Sensitivity Analysis on Reduced Graphene Oxide Field-Effect Transistor
Creators: Chao Wang - University of Iowa
Haihui Pu - University of Chicago
Xiaoyu Sui - University of Chicago
Shiyu Zhou - University of Wisconsin–Madison
Junhong Chen - University of Chicago
Resource Type: Journal article
Publication Details: IEEE transactions on nanotechnology, Vol.20, pp.404-416
Publisher: IEEE
DOI: 10.1109/TNANO.2021.3076135
ISSN: 1536-125X
eISSN: 1941-0085
Grant note: #1727846 / National Science Foundation (10.13039/100000001)
Language: English
Date published: 2021
Academic Unit: Industrial and Systems Engineering
Record Identifier: 9984187081402771

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