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

TL;DR

This paper presents a deep learning approach, specifically a CNN model, to accurately classify graphene's charge density and dielectric environment from Raman spectra, overcoming experimental variations and noise.

Contribution

The study introduces a CNN-based method for classifying graphene Raman spectra by charge density and dielectric environment, demonstrating high accuracy and robustness against noise.

Findings

Achieved 99% classification accuracy with CNN.

CNN models are less sensitive to noise than traditional machine learning.

Data augmentation improves model generalization.

Abstract

The impact of the environment on graphene's properties such as strain, charge density, and dielectric environment can be evaluated by Raman spectroscopy. These environmental interactions are not trivial to determine, since they affect the spectra in overlapping ways. Data preprocessing such as background subtraction and peak fitting is typically used. Moreover, collected spectroscopic data vary due to different experimental setups and 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 dielectric environments. We consider two approaches: deep learning models and machine learning algorithms to classify spectra with slightly different charge density or dielectric environment. These two approaches show similar success rates for high Signal-to-Noise data. However, deep learning models are less sensitive to noise. To improve the accuracy and generalization of all models, we use data augmentation through additive noise and peak shifting. We demonstrated the spectra classification with 99% accuracy using a convolutional neural net (CNN) model. The CNN model is able to classify Raman spectra of graphene with different charge doping levels and even subtle variation in the spectra between graphene on SiO$_2$ and graphene on silanized SiO$_2$. 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.