Large Area Automated Characterisation of Chemical Vapour Deposition Grown Monolayer Transition Metal Dichalcogenides Through Photoluminescence Imaging
T. Severs Millard, A. Genco, E. M. Alexeev, S. Randerson, S. Ahn, A., Jang, H. S. Shin, A. I. Tartakovskii

TL;DR
This paper introduces an automated photoluminescence imaging method for large-area characterization of CVD-grown monolayer TMDs, enabling rapid assessment of material quality and orientation across entire substrates.
Contribution
It presents a novel automated analysis technique using PL imaging and computer vision to evaluate large-area TMD samples, surpassing previous limited-area characterization methods.
Findings
Analyzed 20x20 mm and 11.2x5.8 mm samples with over 100,000 objects identified.
Detected two dominant orientations in WSe2 and four in MoSe2, indicating epitaxial growth patterns.
The method significantly reduces characterization time and aids in optimizing growth conditions.
Abstract
CVD growth is capable of producing multiple single crystal islands of atomically thin TMDs over large area substrates, with potential control of their morphology, lateral size, and epitaxial alignment to substrates with hexagonal symmetry. Subsequent merging of epitaxial domains can lead to single-crystal monolayer sheets - a step towards scalable production of high quality TMDs. For CVD growth to be effectively used for such production it is necessary to be able to rapidly assess the quality of material across entire large area substrates. To date characterisation has been limited to sub 0.1 mm2 areas, where the properties measured are not necessarily representative of an entire sample. Here, we apply photoluminescence (PL) imaging and computer vision techniques to create an automated analysis for large area samples of semiconducting TMDs, measuring the properties of island size,…
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