자료유형  

 학위논문

Control Number  

0017165150

International Standard Book Number  

9798346874058

Dewey Decimal Classification Number  

620.11

Main Entry-Personal Name  

Wei, Jingrui.

Publication, Distribution, etc. (Imprint  

[S.l.] : The University of Wisconsin - Madison., 2024

Publication, Distribution, etc. (Imprint  

Ann Arbor : ProQuest Dissertations & Theses, 2024

Physical Description  

94 p.

General Note  

Source: Dissertations Abstracts International, Volume: 86-06, Section: B.

General Note  

Advisor: Voyles, Paul M.

Dissertation Note  

Thesis (Ph.D.)--The University of Wisconsin - Madison, 2024.

Summary, Etc.  

요약Scanning transmission electron microscopy (STEM) has advanced the field of materials science by enabling atomic-scale structural and functional imaging. Beyond standard 2D imaging, higher dimensional data acquisition and analysis are enabling critical new sample insights. New challenges arise as well from the fast data streaming and big data volume. This thesis will discuss how advanced computational approaches and machine learning (ML) enhance the efficiency of STEM data interpretation with example topics. First, we demonstrate convolutional neural network (CNN) approaches for precise atom localization in high-resolution STEM images, establishing comprehensive benchmarks and investigating the relationship between model characteristics and performance across varying image qualities and content. Second, we introduce a deep learning-based electron counting method for ultrafast 4D-STEM detectors, utilizing a Faster-RCNN architecture to achieve accurate electron event detection at higher electron dose than previous methods. Third, a physics-informed regression model for STEM aberration measurement was developed to provide rapid aberration estimation for fine probe alignment or a starting point for sample phase reconstruction algorithms. Fourth, Z-contrast STEM imaging and 4D-STEM were used to investigate short-range order in high entropy carbides with different compositions and thermal treatment. The thesis concludes with a chapter speculating on future directions for research using ML in electron microscopy. Throughout this work, we highlight the importance of incorporating prior knowledge into the machine learning framework, ranging from data representation to model design, contributing to the broader field of quantitative electron microscopy and automation of STEM analysis.

Subject Added Entry-Topical Term  

Materials science.

Subject Added Entry-Topical Term  

Applied physics.

Subject Added Entry-Topical Term  

Computational physics.

Index Term-Uncontrolled  

Scanning transmission electron microscopy

Index Term-Uncontrolled  

Electron microscopy

Index Term-Uncontrolled  

Machine learning

Index Term-Uncontrolled  

Structure characterization

Index Term-Uncontrolled  

Electron dose

Added Entry-Corporate Name  

The University of Wisconsin - Madison Materials Science and Engineering

Host Item Entry  

Dissertations Abstracts International. 86-06B.

Electronic Location and Access  

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Control Number  

joongbu:657551