Classifying optical microscope images of exfoliated graphene flakes by data-driven machine learning
Source abstract
Abstract
Abstract Machine-learning techniques enable recognition of a wide range of images, complementing human intelligence. Since the advent of exfoliated graphene on SiO2/Si substrates, identification of graphene has relied on imaging by optical microscopy. Here, we develop a data-driven clustering analysis method to automatically identify the position, shape, and thickness of graphene flakes from optical microscope images of exfoliated graphene on an SiO2/Si substrate. Application of the extraction algorithm to optical images yielded optical and morphology feature values for the regions surrounded by the flake edges. The feature values formed discrete clusters in the optical feature space, which were derived from 1-, 2-, 3-, and 4-layer graphene. The cluster centers are detected by the unsupervised machine-learning algorithm, enabling highly accurate classification of monolayer, bilayer, and trilayer graphene. The analysis can be applied to a range of substrates with differing SiO2thicknesses.
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Research context
OpenAlex topics: Graphene research and applications · Force Microscopy Techniques and Applications · Advanced Electron Microscopy Techniques and Applications
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (31)
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