  Two-dimensional (2D) crystals are attracting growing interest in various
research fields such as engineering, physics, chemistry, pharmacy and biology
owing to their low dimensionality and dramatic change of properties compared to
the bulk counterparts. Among the various techniques used to manufacture 2D
crystals, mechanical exfoliation has been essential to practical applications
and fundamental research. However, mechanically exfoliated crystals on
substrates contain relatively thick flakes that must be found and removed
manually, limiting high-throughput manufacturing of atomic 2D crystals and van
der Waals heterostructures. Here we present a deep learning-based method to
segment and identify the thickness of atomic layer flakes from optical
microscopy images. Through carefully designing a neural network based on U-Net,
we found that our neural network based on U-net trained only with the data
based on 24 images successfully distinguish monolayer and bilayer MoS2 with a
success rate of 70%, which is a practical value in the first screening process
for choosing monolayer and bilayer flakes of MoS2 of all flakes on substrates
without human eye. The remarkable results highlight the possibility that a
large fraction of manual laboratory work can be replaced by AI-based systems,
boosting productivity.
