MagNet: machine learning enhanced three-dimensional magnetic reconstruction

Three-dimensional (3D) magnetic reconstruction is vital to the study of novel magnetic materials for 3D spintronics. Vector field electron tomography (VFET) is an important tool to achieve that, but it is challenging due to the missing wedge problem. Conventional analytical algorithms are no longer applicable. On the other hand, model based iterative algorithms require prior knowledge and are expensive in run-time to give satisfactory reconstruction results for each individual sample. In this article, we demonstrate a data-driven U-shaped neural network enhanced VFET algorithm that can give improved reconstructed magnetic induction fields with missing wedge data.