Optical image analysis of WSe2 − thresholding for layer detection

Sahriar, Miah Abdullah; Rumman, Abdul Hamid; Ullah, Ahammad; Barua, Kaushik; Monju, Shohaib Ibne; Jawad, Munim Shahriar; Faisal, Md. Atik; Ahsan, Ridwan Radit; Jang, Houk; Ahmed, Saquib

doi:10.1016/j.commatsci.2025.113888

Optical image analysis of WSe₂ − thresholding for layer detection

Journal Article · Thu Apr 10 00:00:00 EDT 2025 · Computational Materials Science

DOI:https://doi.org/10.1016/j.commatsci.2025.113888· OSTI ID:2569460

Sahriar, Miah Abdullah ^[1]; Rumman, Abdul Hamid ^[2]; Ullah, Ahammad ^[2]; Barua, Kaushik ^[2]; Monju, Shohaib Ibne ^[2]; Jawad, Munim Shahriar ^[2]; Faisal, Md. Atik ^[2]; Ahsan, Ridwan Radit ^[2]; ^[3]; Ahmed, Saquib ^[4]

Univ. of Minnesota, Minneapolis, MN (United States)
Bangladesh University of Engineering and Technology (BUET) (Bangladesh)
Brookhaven National Laboratory (BNL), Upton, NY (United States). Center for Functional Nanomaterials (CFN)
State Univ. of New York (SUNY), Buffalo, NY (United States)

The fast and reliable layer identification of two-dimensional transition metal dichalcogenide (TMD), such as WSe₂, is essential to investigating their thickness-dependent electronic and optical properties. This article presents efficient optical image thresholding methodology designed to segment the mono, bi, and tri-layer regions of WSe₂ flakes mechanically exfoliated onto a SiO₂/Si substrate. The optical images were first preprocessed to exclude the background effect and analyzed using the pixel medians and interquartile ranges for fundamental color channels—red, green, and blue (RGB). The analysis of red channel pixel intensities yielded three distinct ranges, serving as thresholds for layer segmentation: monolayer (111.0–118.0), bilayer (103.0–110.0), and tri-layer (93.0–103.0). Similarly, thresholds were established for each color channel, facilitating a comparative study of the segmentation performances. Further, the intersection-over-union ($IoU$) calculations revealed that the red and green channels demonstrated greater than 99 % and 90 % accuracy in differentiating each layer, respectively. This approach yields remarkable results without substantial data calibration that utilizes time-intensive heuristic techniques. Moreover, the proposed methodology offers the flexibility to compare performances across different color channels, expanding the applicability for other 2D material systems.

View Accepted Manuscript (DOE)

Research Organization:: Brookhaven National Laboratory (BNL), Upton, NY (United States). Center for Functional Nanomaterials (CFN)

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

Grant/Contract Number:: SC0012704

OSTI ID:: 2569460

Report Number(s):: BNL--228266-2025-JAAM

Journal Information:: Computational Materials Science, Journal Name: Computational Materials Science Vol. 253; ISSN 0927-0256

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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36 MATERIALS SCIENCE
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Optical image analysis of WSe2 − thresholding for layer detection

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