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A new exploration of quality testing technique for the wafer-scale graphene film based on the terahertz vector network analysis technology

Authors :
Cheng Chen
Ali Pourkazemi
Wu Zhao
Niko Van den Brande
Tom Hauffman
Zhiyong Zhang
Johan Stiens
Faculty of Engineering
Electronics and Informatics
Laboratorium for Micro- and Photonelectronics
Materials and Chemistry
Materials Characterisation
Electrochemical and Surface Engineering
Source :
Applied Surface Science. 616:156498
Publication Year :
2023
Publisher :
Elsevier BV, 2023.

Abstract

As the fabrication scale of graphene continues to expand, the limitations of existing mass characterization techniques such as Raman spectroscopy, AFM, and other microscopic characterization techniques for large-scale homogeneity characterization of graphene films become more and more prominent. Although the line-shaped defects (primarily grain boundaries and folds) distributed on the graphene film can be observed using the above techniques, it is difficult to comprehensively evaluate their distribution state on the entire film surface using these instruments. Based on this background, the author’s group has been aiming at the above problems by combining terahertz vector network analysis technology with the detection of large-area homogeneity of graphene films and quantitative characterization of line-shaped defects on the film surface, to explore a quality inspection technology that can be used in the production line of graphene films. The results demonstrate that the terahertz transverse electric (TE) wave is extremely sensitive to the variation of local thickness and electrical conductivity, which can be used in the homogeneity testing for the graphene; distributed line-shaped defects on graphene surface can exhibit anisotropic EM behavior with respect to THz TE waves, which can be exploited to identify the defect distribution states.

Details

ISSN :
01694332
Volume :
616
Database :
OpenAIRE
Journal :
Applied Surface Science
Accession number :
edsair.doi.dedup.....22ac08e4d1214b3afb09d5d2cf97ac90