Abstract
In the domain of condensed matter physics the monolayer graphene material is of interest. Researchers have started to develop new quantum circuit models that rely on this material properties. Unfortunately, the process to obtain pieces of monolayer graphene useable for nanocomponent design produces a lot of undesired other structures on the same substrate. In this paper, we have developed an approach to target and detect monolayer graphene from alternating layers of graphene and other particles (corrugated crystals and tape residues). We describe a region of interest-based image segmentation process to extract 2D atomic crystals; however, some unwanted particles remain in the segmented region. An intensity-based discrimination of monolayer graphene from other particles is applied and it is observed that the red color space of the monolayer graphene differs 1.8–6%, green 2.5–8% and blue differ 2.5% to 3% from the surrounding background pixel.
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Balasubramaniyan, S., Parmentier, F., Roulleau, P., Thevenin, M., Brenes, A., Trocan, M. (2021). Detection of Monolayer Graphene. In: Nguyen, N.T., Iliadis, L., Maglogiannis, I., Trawiński, B. (eds) Computational Collective Intelligence. ICCCI 2021. Lecture Notes in Computer Science(), vol 12876. Springer, Cham. https://doi.org/10.1007/978-3-030-88081-1_59
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DOI: https://doi.org/10.1007/978-3-030-88081-1_59
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