Identification of triangular single crystals of transition metal dichalcogenides based on the detection algorithm

Abstract

The distinctive properties and facile integration of 2D materials hold the potential to offer promising avenues for the on-chip photonic devices, and the expeditious and nondestructive identification and localization of diverse fundamental building blocks become key prerequisites. Here, we present a methodology grounded in digital image processing and deep learning, which effectively achieves the detection and precise localization of four monolayer-thick triangular single crystals of transition metal dichalcogenides with the mean average precision above 90%, and the approach demonstrates robust recognition capabilities across varied imaging conditions encompassing both white light and monochromatic light. This stands poised to serve as a potent data-driven tool enhancing the characterizing efficiency and holds the potential to expedite research initiatives and applications founded on the utilization of 2D materials.