Identifying charge density and dielectric environment of graphene using Raman spectroscopy and deep learning

Raman spectroscopy is used to evaluate graphene and its interactions with its surroundings such as strain, charge density, and dielectric environment, reflected in peak positions, shape and intensity. In addition, substrate interference effects and experimental alignment affects collected spectroscopic data causing variation even for similar environments. Such variations, artifacts, and environmental differences pose a challenge in accurate spectral analysis. In this work, we developed a deep learning model to overcome the effects of such variations and classify graphene Raman spectra according to different charge densities and slightly varying dielectric environments. We demonstrated the spectra classification with 99% accuracy using the proposed CNN model. This CNN model is able to classify Raman spectra of graphene with different charge doping levels (< 2X10¹²cm^-2) and even subtle variation in the spectra between graphene on SiO₂ and graphene on silanized SiO₂. Our proposed model shows high reproducibility and stability. Our approach has the potential for fast and reliable estimation of graphene doping levels and dielectric environments. The proposed model paves the way for achieving efficient analytical tools to evaluate the properties of graphene.