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

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.

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• An open-source robust machine learning platform for real-time detection and classification of 2D material flakes
• Classical and quantum regression analysis for the optoelectronic performance of NTCDA/p-Si UV photodiode
• Fully automated identification of 2D material samples
• Machine-learning accelerated identification of exfoliable two-dimensional materials
• Identification and Structural Characterization of Twisted Atomically Thin Bilayer Materials by Deep Learning
• MaskTerial: A Foundation Model for Automated 2D Material Flake Detection