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Advancing Optical Metrology Through ...

Wang, Hao,

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Advancing Optical Metrology Through Computational Imaging Techniques /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Advancing Optical Metrology Through Computational Imaging Techniques // Hao Wang.
Author:	Wang, Hao,
Description:	1 electronic resource (157 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 86-07, Section: B.
Contained By:	Dissertations Abstracts International86-07B.
Subject:	Engineering. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31631580
ISBN:	9798302837578

Advancing Optical Metrology Through Computational Imaging Techniques /
Wang, Hao,

Advancing Optical Metrology Through Computational Imaging Techniques /Hao Wang. - 1 electronic resource (157 pages)

Source: Dissertations Abstracts International, Volume: 86-07, Section: B.

Optical metrology, which uses light to precisely measure and characterize objects, is essential in industrial manufacturing, scientific research, and engineering design. Computational optical imaging techniques have leveraged novel optical systems, advanced algorithms, and increased computing power to accurately reconstruct and analyze the physical properties of objects, surpassing the capabilities of traditional optical metrology methods. This thesis explores the advancement of optical metrology through computational imaging techniques, focusing on the following three primary research areas.The first research focuses on semiconductor chip surface topography measurement, which plays a critical role in various semiconductor industrial applications. Conventional optical metrology techniques often face challenges in balancing field-of-view(FOV) and spatial resolution. To overcome these limitations, I propose a novel technique termed Fourier Ptychographic Topography (FPT). FPT provides wide FOV, high resolution, nanoscale height reconstruction accuracy. Experimental validation on a sample semiconductor chip also demonstrates the robustness and superior performance of FPT compared to current standard optical profilometry methods.In the second study, I extend my research from surface metrology to large-scale 3D particle detection from a single hologram. Traditional 3D particle localization methods often rely on single-scattering models, which are limited in their ability to accurately capture dense particle distributions at deep imaging depths. To address this limitation, I use a multiple-scattering beam propagation method (BPM) to model multiple scattering effect of 3D particle fields. I achieve robust detection of dense 3D particles and experimental results demonstrate that my approach provides significantly higher accuracy compared to conventional single-scattering models, particularly in scenarios with high particle densities and deep imaging depths.Finally, I explore the application of deep learning methods for phase retrieval, a fundamental problem in computational imaging-based optical metrology. My previous research utilized physical models to simulate the optical imaging process. While these traditional model-based methods achieved state-of-the-art results in many applications, the development of deep learning methods introduces new possibilities for solving highly ill-posed problems that were previously unsolvable. In my third study, I introduce the Neural Phase Retrieval (NeuPh) network, which enables flexible object representations and resolution-enhanced phase reconstruction from multiplexedFourier ptychographic microscopy (FPM). Experimental results demonstrate the NeuPh network's scalability, robustness, accuracy, and generalizability in solving the highly ill-posed phase retrieval problem, outperforming existing methods and offering potential wide applications in optical metrology techniques.Through these investigations, this thesis advances optical metrology by integrating computational imaging techniques, offering improved measurement accuracy, scalability, robustness, and paving the way for enhanced optical metrology methodologies in various applications.

English

ISBN: 9798302837578Subjects--Topical Terms:

586835
Engineering.
Subjects--Index Terms:

Beam propagation method

Advancing Optical Metrology Through Computational Imaging Techniques /
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500 $a Advisors: Tian, Lei Committee members: Goyal, Vivek K.; Konrad, Janusz; Sander, Michelle.
502 $b Ph.D. $c Boston University $d 2025.
520 $a Optical metrology, which uses light to precisely measure and characterize objects, is essential in industrial manufacturing, scientific research, and engineering design. Computational optical imaging techniques have leveraged novel optical systems, advanced algorithms, and increased computing power to accurately reconstruct and analyze the physical properties of objects, surpassing the capabilities of traditional optical metrology methods. This thesis explores the advancement of optical metrology through computational imaging techniques, focusing on the following three primary research areas.The first research focuses on semiconductor chip surface topography measurement, which plays a critical role in various semiconductor industrial applications. Conventional optical metrology techniques often face challenges in balancing field-of-view(FOV) and spatial resolution. To overcome these limitations, I propose a novel technique termed Fourier Ptychographic Topography (FPT). FPT provides wide FOV, high resolution, nanoscale height reconstruction accuracy. Experimental validation on a sample semiconductor chip also demonstrates the robustness and superior performance of FPT compared to current standard optical profilometry methods.In the second study, I extend my research from surface metrology to large-scale 3D particle detection from a single hologram. Traditional 3D particle localization methods often rely on single-scattering models, which are limited in their ability to accurately capture dense particle distributions at deep imaging depths. To address this limitation, I use a multiple-scattering beam propagation method (BPM) to model multiple scattering effect of 3D particle fields. I achieve robust detection of dense 3D particles and experimental results demonstrate that my approach provides significantly higher accuracy compared to conventional single-scattering models, particularly in scenarios with high particle densities and deep imaging depths.Finally, I explore the application of deep learning methods for phase retrieval, a fundamental problem in computational imaging-based optical metrology. My previous research utilized physical models to simulate the optical imaging process. While these traditional model-based methods achieved state-of-the-art results in many applications, the development of deep learning methods introduces new possibilities for solving highly ill-posed problems that were previously unsolvable. In my third study, I introduce the Neural Phase Retrieval (NeuPh) network, which enables flexible object representations and resolution-enhanced phase reconstruction from multiplexedFourier ptychographic microscopy (FPM). Experimental results demonstrate the NeuPh network's scalability, robustness, accuracy, and generalizability in solving the highly ill-posed phase retrieval problem, outperforming existing methods and offering potential wide applications in optical metrology techniques.Through these investigations, this thesis advances optical metrology by integrating computational imaging techniques, offering improved measurement accuracy, scalability, robustness, and paving the way for enhanced optical metrology methodologies in various applications.
546 $a English
590 $a School code: 0017
650 4 $a Engineering. $3 586835
650 4 $a Optics. $3 517925
650 4 $a Biomedical engineering. $3 535387
650 4 $a Materials science. $3 543314
653 $a Beam propagation method
653 $a Optical metrology
653 $a Fourier Ptychographic Topography
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690 $a 0752
690 $a 0794
690 $a 0541
710 2 $a Boston University. $b Electrical & Computer Engineering ENG. $e degree granting institution. $3 3770549
720 1 $a Tian, Lei $e degree supervisor.
773 0 $t Dissertations Abstracts International $g 86-07B.
790 $a 0017
791 $a Ph.D.
792 $a 2025
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