Identification and Structural Characterization of Twisted Atomically Thin Bilayer Materials by Deep Learning.

ABSTRACT: 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 (MoS₂) and the application of a semantic segmentation convolutional neural network (CNN) to accurately and rapidly identify thicknesses of MoS₂ 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,

SUBMITTER: Yang H 

PROVIDER: S-EPMC10921996 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature