Robust multi-scale multi-feature deep learning for atomic and defect identification in Scanning Tunneling Microscopy on H-Si(100) 2x1 surface

TL;DR

This paper presents a robust deep learning framework for identifying atoms and defects in STM images of H-Si(100) surfaces, capable of handling noise, contaminants, and complex surface features, and extends to unsupervised defect classification.

Contribution

The authors develop a multi-network deep learning approach for automated atomic and defect identification in STM images, including an unsupervised defect classification method, applicable to various surfaces.

Findings

Effective identification of atomic species and defects in noisy STM images.

Automated workflow successfully deployed on operational STM platform.

Unsupervised defect classification using mean-shift clustering enhances defect analysis.

Abstract

The nature of the atomic defects on the hydrogen passivated Si (100) surface is analyzed using deep learning and scanning tunneling microscopy (STM). A robust deep learning framework capable of identifying atomic species, defects, in the presence of non-resolved contaminates, step edges, and noise is developed. The automated workflow, based on the combination of several networks for image assessment, atom-finding and defect finding, is developed to perform the analysis at different levels of description and is deployed on an operational STM platform. This is further extended to unsupervised classification of the extracted defects using the mean-shift clustering algorithm, which utilizes features automatically engineered from the combined output of neural networks. This combined approach allows the identification of localized and extended defects on the topographically non-uniform surfaces or real materials. Our approach is universal in nature and can be applied to other surfaces for building comprehensive libraries of atomic defects in quantum materials.