Deep-learning-based semantic image segmentation of graphene field-effect transistors

Abstract

Abstract Large-scale graphene films are available, which enables the integration of graphene field-effect transistor (G-FET) arrays on chips. However, the transfer characteristics are not identical but diverse over the array. Optical microscopy is widely used to inspect G-FETs, but quantitative evaluation of the optical images is challenging as they are not classified. Here, we implemented a deep-learning-based semantic image segmentation algorithm. Through a neural network, every pixel was assigned to graphene, electrode, substrate, or contaminants, with exceeding a success rate of 80%. We also found that the drain current and transconductance correlated with the coverage of graphene films.