Three-dimensional (3D) magnetic reconstruction is vital to the study of novel magnetic materials for 3D spintronics. Vector field electron tomography (VFET) is a major in house tool to achieve that. However, conventional VFET reconstruction exhibits significant artefacts due to the unavoidable presence of missing wedges. In this article, we propose a deep-learning enhanced VFET method to address this issue. A magnetic textures library is built by micromagnetic simulations. MagNet, an U-shaped convolutional neural network, is trained and tested with dataset generated from the library. We demonstrate that MagNet outperforms conventional VFET under missing wedge. Quality of reconstructed magnetic induction fields is significantly improved.
@article{arxiv.2210.03066,
title = {MagNet: machine learning enhanced three-dimensional magnetic reconstruction},
author = {Boyao Lyu and Shihua Zhao and Yibo Zhang and Weiwei Wang and Haifeng Du and Jiadong Zang},
journal= {arXiv preprint arXiv:2210.03066},
year = {2022}
}