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MagNet: machine learning enhanced three-dimensional magnetic reconstruction

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arxiv 2210.03066 v1 pith:O2ATCXY3 submitted 2022-10-06 cond-mat.mtrl-sci

Boyao Lyu , Shihua Zhao , Yibo Zhang , Weiwei Wang , Haifeng Du , Jiadong Zang This is my paper
classification cond-mat.mtrl-sci
keywords magneticvfetmagnetreconstructionconventionalenhancedlibrarymissing
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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.

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