Enhancing Multi-Tip Artifact Detection in STM Images Using Fourier Transform and Vision Transformers

Abstract

We address the issue of multi-tip artifacts in Scanning Tunneling Microscopy (STM) images by applying the fast Fourier transform (FFT) as a feature engineering method. We fine-tune various neural network architectures using a synthetic dataset, including Vision Transformers (ViT). The FFT-based preprocessing significantly improves the performance of ViT models compared to using only the grayscale channel. Ablation experiments highlight the optimal conditions for synthetic dataset generation. Unlike traditional methods that are challenging to implement for large datasets and used offline, our method enables on-the-fly classification at scale. Our findings demonstrate the efficacy of combining the Fourier transform with deep learning for enhanced artifact detection in STM images, contributing to more accurate analysis in material science research.