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Practical applications of graphene require a reliable high-throughput method of graphene identification and
quality control, which can be used for large-scale substrates and wafers. We have proposed and
experimentally tested a fast and fully automated approach for determining the number of atomic planes in
graphene samples. It is based on an original image processing algorithm, which utilizes micro-Raman
calibration, light background subtraction, lighting non-uniformity correction, and color and grayscale image
processing for each pixel. Our approach works for various substrates and can be applied to
mechanically exfoliated, chemically derived, deposited or epitaxial graphene on an industrial scale.
Graphene, a monolayer of carbon atoms, is a high-interest material
in the research community and semiconductor industry due to its
extraordinary electronic, thermal, and mechanical properties.
Graphene layer identification is very important since its intrinsic
properties change drastically between each 0.34-nm thick layer.
Current methods of identification rely on restrictive small-area
microscopy techniques, the most robust being micro-Raman
spectroscopy. Here we present a new method for a large-area
graphene layer identification characterized by low cost, high
accuracy, high throughput, complete automation, and scalability.
Our metrology tool is based on a fast image processing algorithm,
which analyzes optical contrasts between single-layer, bi-layer,
and few-layer graphene used for exfoliated, transferred, or grown
graphene flakes on large wafers verified by micro-Raman
spectroscopy.
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