Versatile recognition of graphene layers from optical images under controlled illumination through green channel correlation method

Sahriar, Miah Abdullah; Abed, Mohd. Rakibul Hasan; Nirjhar, Ahsiur Rahman; Dipon, Nazmul Ahsan; Tan-Ema, Sadika Jannath; Somphonsane, Ratchanok; Buapan, Kanokwan; Wei, Yong; Ramamoorthy, Harihara; Jang, Houk; Nam, Chang-Yong; Ahmed, Saquib

doi:10.1088/1361-6528/ace979

Versatile recognition of graphene layers from optical images under controlled illumination through green channel correlation method

Journal Article · Thu Aug 17 00:00:00 EDT 2023 · Nanotechnology

DOI:https://doi.org/10.1088/1361-6528/ace979· OSTI ID:1999164

^[1]; Abed, Mohd. Rakibul Hasan ^[1]; ^[1]; Dipon, Nazmul Ahsan ^[1]; Tan-Ema, Sadika Jannath ^[1]; Somphonsane, Ratchanok ^[2]; Buapan, Kanokwan ^[2]; Wei, Yong ^[3]; ^[2]; ^[4]; ^[4]; ^[5]

Bangladesh University of Engineering and Technology (BUET), Dhaka (Bangladesh)
King Mongkut's Institute of Technology Ladkrabang, Bangkok (Thailand)
High Point University, NC (United States)
Brookhaven National Laboratory (BNL), Upton, NY (United States)
SUNY—Buffalo State, NY (United States)

In this study, a simple yet versatile method is proposed for identifying the number of exfoliated graphene layers transferred on an oxide substrate from optical images, utilizing a limited number of input images for training, paired with a more traditional number of a few thousand well-published Github images for testing and predicting. Two thresholding approaches, namely the standard deviation-based approach and the linear regression-based approach, were employed in this study. The method specifically leverages the red, green, and blue color channels of image pixels and creates a correlation between the green channel of the background and the green channel of the various layers of graphene. This method proves to be a feasible alternative to deep learning-based graphene recognition and traditional microscopic analysis. The proposed methodology performs well under conditions where the effect of surrounding light on the graphene-on-oxide sample is minimum and allows rapid identification of the various graphene layers. Here, the study additionally addresses the functionality of the proposed methodology with nonhomogeneous lighting conditions, showcasing successful prediction of graphene layers from images that are lower in quality compared to typically published in literature. In all, the proposed methodology opens up the possibility for the non-destructive identification of graphene layers from optical images by utilizing a new and versatile method that is quick, inexpensive, and works well with fewer images that are not necessarily of high quality.

View Accepted Manuscript (DOE)

Research Organization:: Brookhaven National Laboratory (BNL), Upton, NY (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Basic Energy Sciences (BES). Scientific User Facilities (SUF)

Grant/Contract Number:: SC0012704

OSTI ID:: 1999164

Report Number(s):: BNL--224774-2023-JAAM

Journal Information:: Nanotechnology, Journal Name: Nanotechnology Journal Issue: 44 Vol. 34; ISSN 0957-4484

Publisher:: IOP PublishingCopyright Statement

Country of Publication:: United States

Language:: English

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36 MATERIALS SCIENCE
RGB
graphene layer recognition
green channel
illumination condition
linear regression-based threshold
pixel accuracy ratio
standard deviation-based threshold

Versatile recognition of graphene layers from optical images under controlled illumination through green channel correlation method

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