Two-dimensional (2D) materials and
their in-plane and out-of-plane
(i.e., van der Waals, vdW) heterostructures
are promising building blocks for next-generation electronic and optoelectronic
devices. Since the performance of the devices is strongly dependent
on the crystalline quality of the materials and the interface characteristics
of the heterostructures, a fast and nondestructive method for distinguishing
and characterizing various 2D building blocks is desirable to promote
the device integrations. In this work, based on the color space information
on 2D materials’ optical microscopy images, an artificial neural
network-based deep learning algorithm is developed and applied to
identify eight kinds of 2D materials with accuracy well above 90%
and a mean value of 96%. More importantly, this data-driven method
enables two interesting functionalities: (1) resolving the interface
distribution of chemical vapor deposition (CVD) grown in-plane and
vdW heterostructures and (2) identifying defect concentrations of
CVD-grown 2D semiconductors. The two functionalities can be utilized
to quickly identify sample quality and optimize synthesis parameters
in the future. Our work improves the characterization efficiency of
atomically thin materials and is therefore valuable for their research
and applications.