Probing Oxygen Vacancy Distribution in Oxide Heterostructure by Deep Learning-based Spectral Analysis of Current Noise
Applied Surface Science, 2022
This work introduces a deep learning-based analysis of current noise through the 2DEG at LaAlO3/SrTiO3 interfaces to quantitatively estimate the spatial distribution of oxygen vacancies with nanoscale precision, revealing that vacancies are uniformly spread over ~100 nm in as-grown heterostructures but can be electrically confined to within ~14 nm of the interface at room temperature.
BibTeX
@article{lee2022probing,
title={Probing oxygen vacancy distribution in oxide heterostructures by deep Learning-based spectral analysis of current noise},
author={Lee, Sunwoo and Jeon, Jaeyoung and Lee, Hyungwoo},
journal={Applied Surface Science},
volume={604},
pages={154599},
year={2022},
publisher={Elsevier}
}