Spatial distribution quantification and control of ink flakes in reduced graphene oxide FET inkjet printing

Jaesung Lee; Chao Wang; Shiyu Zhou; Junhong Chen

doi:10.1016/j.promfg.2019.06.109

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Spatial distribution quantification and control of ink flakes in reduced graphene oxide FET inkjet printing

Journal article

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Spatial distribution quantification and control of ink flakes in reduced graphene oxide FET inkjet printing

Jaesung Lee, Chao Wang, Shiyu Zhou and Junhong Chen

Procedia manufacturing, Vol.34, pp.19-25

2019

DOI: 10.1016/j.promfg.2019.06.109

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Abstract

Inkjet printing for field-effect transistor (FET) sensor fabrication with graphene oxide (GO) is promising because of its attractive features including low costs and scalability. However, the process variation in inkjet printing poses difficulties in achieving stable and high quality FET sensors. In this paper, we focus on the GO flakes variation on the substrate after the ink drying, especially for quantifying and controlling the flake distribution on the FET channel. A spatial statistical model describing the flake center distribution is first developed using Poisson process. Based on this model, we build an optimization framework to achieve the best flake distribution that satisfies FET performance requirement. In the optimization framework, we focus on two key characteristics in the GO ink production process, the flake size and flake concentration, and the exact solutions for these two characteristics are derived. The relationship between the characteristics and the flake distribution is demonstrated and discussed through various simulations. The comparisons between simulation results and the exact solution verify the effectiveness of the proposed methods.

Details

Title: Subtitle: Spatial distribution quantification and control of ink flakes in reduced graphene oxide FET inkjet printing
Creators: Jaesung Lee - University of Wisconsin–Madison
Chao Wang - University of Wisconsin–Madison
Shiyu Zhou - University of Wisconsin–Madison
Junhong Chen - University of Wisconsin–Milwaukee
Resource Type: Journal article
Publication Details: Procedia manufacturing, Vol.34, pp.19-25
DOI: 10.1016/j.promfg.2019.06.109
ISSN: 2351-9789
eISSN: 2351-9789
Language: English
Date published: 2019
Academic Unit: Industrial and Systems Engineering
Record Identifier: 9984186971602771

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