A simple and robust machine learning assisted process flow for the layer number identification of TMDs using optical contrast spectroscopy

Abstract

Layered transition metal dichalcogenides (TMDs) like tungsten disulphide (WS2) possess a large direct electronic band gap (∼2 eV) in the monolayer limit, making them ideal candidates for opto-electronic applications. The size and nature of the bandgap is strongly dependent on the number of layers. However, different TMDs require different experimental tools under specific conditions to accurately determine the number of layers. Here, we identify the number of layers of WS2 exfoliated on top of SiO2/Si wafer from optical images using the variation of optical contrast with thickness. Optical contrast is a universal feature that can be easily extracted from digital images. But fine variations in the optical images due to different capturing conditions often lead to inaccurate layer number determination. In this paper, we have implemented a simple Machine Learning assisted image processing workflow that uses image segmentation to eliminate this difficulty. The workflow developed for WS2 is also demonstrated on MoS2, graphene and h–BN, showing its applicability across various classes of 2D materials. A graphical user interface is provided to enhance the adoption of this technique in the 2D materials research community.