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• W2919284498 abstract "Abstract Machine-learning techniques enable recognition of a wide range of images, complementing human intelligence. Since the advent of exfoliated graphene on SiO 2 /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 SiO 2 /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 SiO 2 thicknesses." @default.
• W2919284498 created "2019-03-11" @default.
• W2919284498 creator A5019310996 @default.
• W2919284498 creator A5037486405 @default.
• W2919284498 date "2019-01-18" @default.
• W2919284498 modified "2023-10-17" @default.
• W2919284498 title "Classifying optical microscope images of exfoliated graphene flakes by data-driven machine learning" @default.
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• W2919284498 doi "https://doi.org/10.1038/s41699-018-0084-0" @default.
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