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Revealing the Three-Dimensional Magn...

Zhao, Shihua.

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Revealing the Three-Dimensional Magnetic Texture with Machine Learning Models.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Revealing the Three-Dimensional Magnetic Texture with Machine Learning Models./
作者:	Zhao, Shihua.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2023,
面頁冊數:	153 p.
附註:	Source: Dissertations Abstracts International, Volume: 84-08, Section: B.
Contained By:	Dissertations Abstracts International84-08B.
標題:	Condensed matter physics. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30248550
ISBN:	9798374405637

Revealing the Three-Dimensional Magnetic Texture with Machine Learning Models.
Zhao, Shihua.

Revealing the Three-Dimensional Magnetic Texture with Machine Learning Models. - Ann Arbor : ProQuest Dissertations & Theses, 2023 - 153 p.

Source: Dissertations Abstracts International, Volume: 84-08, Section: B.

Thesis (Ph.D.)--City University of New York, 2023.

Revealing three-dimensional (3D) magnetic textures with vector field electron tomography (VFET) is essential in studying novel magnetic materials with topologically protected spin textures potentially being used in the next-generation semiconductor industry. In this dissertation, we use machine learning (ML) models to reconstruct 3D magnetic textures from electron holography (EH) data.We can feed the EH data, a series of two-dimensional (2D) phasemaps, into a neural network (NN) architecture directly or feed the EH data into a conventional VFET and then feed the reconstructed results into a NN. Thus, perceptive NN, either a simple convolutional neural network (CNN) or Unet architecture, is built and used to reconstruct the 3D magnetic texture. We demonstrate that the magnetic vector potential and magnetic induction field can be successfully reconstructed with an end-to-end Unet-based ML model. Also, reconstruction results of conventional VFET can be significantly enhanced with a plug-and-play Unet attached to it. The scaling law for run time versus dataset size is studied. Reconstruction results of EH data with various defects, such as noise, sparsity, misalignment, and missing wedge, are also discussed in the frame of ML models with Unet architecture.Furthermore, a generative model is introduced to reconstruct the magnetization to solve the missing information of scalar potential that EH cannot probe. Integrating the cycle consistency and a forward model from magnetization to EH phasemap, we build a cycle consistency generative adversarial network (cycleGAN) based generative model that gives impressive reconstruction results of magnetization. This cycle consistency with a forward model generative model framework is also a promising solution for other inverse problems with an explicit forward model.

ISBN: 9798374405637Subjects--Topical Terms:

3173567
Condensed matter physics.
Subjects--Index Terms:

3D magnetic reconstruction

Revealing the Three-Dimensional Magnetic Texture with Machine Learning Models.
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520 $a Revealing three-dimensional (3D) magnetic textures with vector field electron tomography (VFET) is essential in studying novel magnetic materials with topologically protected spin textures potentially being used in the next-generation semiconductor industry. In this dissertation, we use machine learning (ML) models to reconstruct 3D magnetic textures from electron holography (EH) data.We can feed the EH data, a series of two-dimensional (2D) phasemaps, into a neural network (NN) architecture directly or feed the EH data into a conventional VFET and then feed the reconstructed results into a NN. Thus, perceptive NN, either a simple convolutional neural network (CNN) or Unet architecture, is built and used to reconstruct the 3D magnetic texture. We demonstrate that the magnetic vector potential and magnetic induction field can be successfully reconstructed with an end-to-end Unet-based ML model. Also, reconstruction results of conventional VFET can be significantly enhanced with a plug-and-play Unet attached to it. The scaling law for run time versus dataset size is studied. Reconstruction results of EH data with various defects, such as noise, sparsity, misalignment, and missing wedge, are also discussed in the frame of ML models with Unet architecture.Furthermore, a generative model is introduced to reconstruct the magnetization to solve the missing information of scalar potential that EH cannot probe. Integrating the cycle consistency and a forward model from magnetization to EH phasemap, we build a cycle consistency generative adversarial network (cycleGAN) based generative model that gives impressive reconstruction results of magnetization. This cycle consistency with a forward model generative model framework is also a promising solution for other inverse problems with an explicit forward model.
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