2024/02/26 by Haitao Yang, Ruiqi Hu, Heng Wu +13 · 1 voice · 16 citations
Chemistry · Materials Science · Physics and Astronomy · #2D Materials and Applications #Bilayer #Characterization (materials science) #Chemistry #Electronic and Structural Properties of Oxides #Identification (biology) #Machine Learning in Materials Science #Materials science #Membrane #Nanotechnology #cond-mat.mtrl-sci
paper · pdf · doi:10.1021/acs.nanolett.3c04815
published in Nano Letters 24(9), 2789-2797 (American Chemical Society)
openalex publication_date 2024/02/26 · openalex created_date 2025/10/10 · arxiv published 2026/04/17 · arxiv updated 2026/04/17 · openalex updated_date 2026/08/06
Two-dimensional materials are expected to play an important role in next-generation electronics and optoelectronic devices. Recently, twisted bilayer graphene and transition metal dichalcogenides have attracted significant attention due to their unique physical properties and potential applications. In this study, we describe the use of optical microscopy to collect the color space of chemical vapor deposition (CVD) of molybdenum disulfide (MoS 2 ) and the application of a semantic segmentation convolutional neural network (CNN) to accurately and rapidly identify thicknesses of MoS 2 flakes. A second CNN model is trained to provide precise predictions on the twist angle of CVD-grown bilayer flakes. This model harnessed a data set comprising over 10,000 synthetic images, encompassing geometries spanning from hexagonal to triangular shapes. Subsequent validation of the deep learning predictions on twist angles was executed through the second harmonic generation and Raman spectroscopy. Our results introduce a scalable methodology for automated inspection of twisted atomically thin CVD-grown bilayers.