Intelligent Identification of Two-Dimensional Structure by Machine-Learning Optical Microscopy

TL;DR

This paper introduces a machine-learning based optical microscopy method for accurate, large-area identification of 2D materials and heterostructures, improving characterization of properties like thickness and stacking order.

Contribution

The study presents a novel machine-learning optical identification (MOI) method that enhances the accuracy and efficiency of characterizing 2D materials at wafer scale.

Findings

Enables precise identification of 2D material properties

Allows large-area, intelligent characterization

Distinguishes stacking order and impurities

Abstract

Two-dimensional (2D) materials and their heterostructures, with wafer-scale synthesis methods and fascinating properties, have attracted numerous interest and triggered revolutions of corresponding device applications. However, facile methods to realize accurate, intelligent and large-area characterizations of these 2D structures are still highly desired. Here, we report a successful application of machine-learning strategy in the optical identification of 2D structure. The machine-learning optical identification method (MOI method) endows optical microscopy with intelligent insight into the characteristic colour information in the optical photograph. Experimental results indicate that the MOI method enables accurate, intelligent and large-area characterizations of graphene, molybdenum disulphide (MoS2) and their heterostructures, including identifications of the thickness, the existence of impurities, and even the stacking order. Thanks to the convergence of artificial intelligence and nanoscience, this intelligent identification method can certainly promote the fundamental research and wafer-scale device application of 2D structures.