论文标题
磁铁：机器学习增强了三维磁重建
MagNet: machine learning enhanced three-dimensional magnetic reconstruction
论文作者
论文摘要
三维（3D）磁重建对于研究3D Spintronics的新型磁性材料至关重要。向量场电子断层扫描（VFET）是实现这一目标的主要工具。但是，由于不可避免地存在缺失的楔子，传统的VFET重建表现出重要的伪像。在本文中，我们提出了一种深入学习增强的VFET方法来解决此问题。磁纹理库是由微磁模拟构建的。磁铁是一个U形卷积神经网络，对图书馆生成的数据集进行了训练和测试。我们证明，磁铁在缺失的楔形下优于常规VFET。重建的磁感应场的质量得到显着提高。
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.
