  Two-dimensional (2D) materials have attracted extensive attention due to
their unique characteristics and application potentials. Raman spectroscopy, as
a rapid and non-destructive probe, exhibits distinct features and holds notable
advantages in the structural characterization of 2D materials. However,
traditional data analysis of Raman spectra relies on manual interpretation and
feature extraction, which are both time-consuming and subjective. In this work,
we employ deep learning techniques, including classificatory and generative
deep learning, to assist the analysis of Raman spectra of typical 2D materials.
For the limited and unevenly distributed Raman spectral data, we propose a data
augmentation approach based on Denoising Diffusion Probabilistic Models (DDPM)
to augment the training dataset and construct a four-layer Convolutional Neural
Network (CNN) for 2D material classification. Experimental results illustrate
the effectiveness of DDPM in addressing data limitations and significantly
improved classification model performance. The proposed DDPM-CNN method shows
high reliability, with 100%classification accuracy. Our work demonstrates the
practicality of deep learning-assisted Raman spectroscopy for high-precision
recognition and classification of 2D materials, offering a promising avenue for
rapid and automated spectral analysis.
