  Identification of the mechanically exfoliated graphene flakes and
classification of the thickness is important in the nanomanufacturing of
next-generation materials and devices that overcome the bottleneck of Moore's
Law. Currently, identification and classification of exfoliated graphene flakes
are conducted by human via inspecting the optical microscope images. The
existing state-of-the-art automatic identification by machine learning is not
able to accommodate images with different backgrounds while different
backgrounds are unavoidable in experiments. This paper presents a deep learning
method to automatically identify and classify the thickness of exfoliated
graphene flakes on Si/SiO2 substrates from optical microscope images with
various settings and background colors. The presented method uses a
hierarchical deep convolutional neural network that is capable of learning new
images while preserving the knowledge from previous images. The deep learning
model was trained and used to classify exfoliated graphene flakes into
monolayer (1L), bi-layer (2L), tri-layer (3L), four-to-six-layer (4-6L),
seven-to-ten-layer (7-10L), and bulk categories. Compared with existing machine
learning methods, the presented method possesses high accuracy and efficiency
as well as robustness to the backgrounds and resolutions of images. The results
indicated that our deep learning model has accuracy as high as 99% in
identifying and classifying exfoliated graphene flakes. This research will shed
light on scaled-up manufacturing and characterization of graphene for advanced
materials and devices.
