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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 陳亮嘉 | zh_TW |
dc.contributor.advisor | Liang-Chia Chen | en |
dc.contributor.author | 盛杉奕 | zh_TW |
dc.contributor.author | Sanjeev Kumar Singh | en |
dc.date.accessioned | 2023-10-03T17:24:47Z | - |
dc.date.available | 2023-11-10 | - |
dc.date.copyright | 2023-10-03 | - |
dc.date.issued | 2023 | - |
dc.date.submitted | 2023-08-14 | - |
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dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/90740 | - |
dc.description.abstract | 此論文旨於通過創新技術來提升點掃描色散共焦顯微鏡(PSCCM)的成像能力。主要目標是使用深度學習的維納去卷積和基於波動理論的標量繞射參數模型,來增強橫向和軸向的解析度。
為了克服PSCCM的解析度限制,我們應用了深度學習的維納去卷積。使用深度學習開發了一種強大的去卷積算法,有效地提高了成像質量並增強了橫向和軸向的解析度。 此外,引入了一個標量繞射參數模型以進一步增強PSCCM的解析度。該模型考慮了由光學元件引起的繞射效應,並利用優化技術來優化成像系統。所提出的模型在橫向和軸向的解析度方面都顯示出了顯著的改進,推動了PSCCM性能的極限。 透過使用合成和真實的樣品進行廣泛的實驗評估,驗證了所提出技術的有效性。成功地測量和描述了各種具有挑戰性的樣品結構,例如1微米的階高、公稱尺寸為W8/S8的淺型光柵結構以及25微米的凸塊。所獲得的結果展示了該方法對於準確分析這些複雜結構的能力,顯著地提高了成像解析度,並使得微觀特徵的詳細可視化和分析成為可能。經實驗結果驗證,透過採用超過50微米閥值的點擴散函數(PSFs),深度學習的去卷積過程將更有能力處理更廣泛的結構範疇,包括那些小於50微米範圍的三維結構。這種策略性的擴展無疑將有助於量測的準確性,提供了一個更全面和綜合的解決方案,用於尺寸分析。 通過整合深度學習的維納去卷積和一個標量繞射參數模型,此論文為PSCCM領域做出了貢獻,並為生物醫學成像、材料科學和納米技術領域的應用打開了新的可能性。所提出的技術為解析度的大幅改進提供了巨大的潛力,增強了PSCCM在廣泛的研究和工業應用中的潛力。 | zh_TW |
dc.description.abstract | This thesis focuses on advancing the imaging capabilities of point scanning chromatic confocal microscopy (PSCCM) through innovative techniques. The main goal is to enhance both lateral and axial resolution using deep learning Weiner deconvolution and a wave theory-based scalar diffraction parametric model.
To overcome the resolution limitations of PSCCM, deep learning Weiner deconvolution is applied. A robust deconvolution algorithm is developed using deep learning, effectively improving the imaging quality and enhance lateral and axial resolution. In addition, a scalar diffraction parametric model is introduced to further enhance PSCCM's resolution. This model accounts for diffraction effects caused by optical elements and utilizes optimization techniques to optimize the imaging system. The proposed model demonstrates remarkable improvements in both lateral and axial resolution, pushing the boundaries of PSCCM performance. The effectiveness of the proposed techniques is validated through extensive experimental evaluations using synthetic and real-world samples. Various challenging sample structures, such as a 1 µm step height, a shallow grating structure with a nominal dimension of W8/S8, and a 25 µm bump, are successfully measured and characterized. The obtained results showcase the method's capability to accurately analyze these complex structures, significantly advancing imaging resolution and enabling detailed visualization and analysis of microscopic features. By incorporating PSFs for heights beyond the 50 μm threshold, the deep learning deconvolution process would be better equipped to tackle a broader spectrum of structures, including those below the 50 μm range. This strategic expansion would undoubtedly contribute to the refinement and accuracy of the measurement process, offering a more holistic and comprehensive solution for dimensional analysis. By integrating deep learning Weiner deconvolution and a scalar diffraction parametric model, this thesis contributes to the field of PSCCM and opens up new possibilities for applications in biomedical imaging, material science, and nanotechnology. The proposed techniques offer substantial resolution improvements, enhancing PSCCM's potential for a wide range of research and industrial applications. | en |
dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2023-10-03T17:24:47Z No. of bitstreams: 0 | en |
dc.description.provenance | Made available in DSpace on 2023-10-03T17:24:47Z (GMT). No. of bitstreams: 0 | en |
dc.description.tableofcontents | Abstract i
摘要 iii List of Figures ix List of Tables xvi List of Abbreviations xviii Chapter 1 Introduction 1 1.1 Background 1 1.2 Motivation, problem, and purpose 4 1.3 Structure of this dissertation 5 Chapter 2 Literature review 7 2.1 Overview 7 2.2 Confocal microscopy 9 2.3 Chromatic confocal microscopy (CCM) 17 2.4 Interferometry microscopy 27 2.5 Chromatic confocal spectral interferometry (CCSI) 30 2.6 Chromatic confocal triangulation sensors 32 2.7 Conclusion to the literature review 34 Chapter 3 Theoretical Background 36 3.1 Overview 36 3.2 Scaler diffraction 36 3.3 Monochromatic field and irradiance 38 3.4 Rayleigh-Sommerfeld solution I 40 3.5 Fresnel approximation 42 3.6 Fraunhofer approximation 43 3.7 Seidel Polynomial 44 3.8 Encoder and decoder deep learning model 46 3.9 Loss in the deep learning model 49 3.10 Activation function in deep learning 51 Chapter 4 Modeling, Simulation and Validation of Chromatic Confocal Microscopy 53 4.1 Scope of this chapter 53 4.2 Geometrical optics 53 4.3 Diffraction optics 58 4.3.1 Coherent imaging theory 59 4.3.2 Incoherent imaging theory 61 4.3.3 Pupil function 62 4.3.4 Imaging transfer function (ITF) and coherent transfer function (CTF) 63 4.3.5 Point spread function (PSF) 64 4.4 Model validation 65 4.5 Derivation of CCM Model 69 Chapter 5 System Design and Implementation of Chromatic Confocal Microscope 93 5.1 Overview 93 5.2 System design 93 5.3 Setup of developed PSCCM 96 5.3.1 Light source 96 5.3.2 Design and simulation of lighting module 100 5.3.3 Illumination and detection pinhole 102 5.3.4 Chromatic confocal objective 102 5.3.5 Reflected path module 108 5.3.6 Sensing module 109 5.4 Zemax integration of the developed confocal system 115 5.5 Commercial spectrometer 116 5.6 Scanning stage 118 5.7 Control software and GUI 119 5.8 Comparison of commercial and experimental spectrum 120 5.9 Algorithm and system calibration 122 5.9.1 Peak detection 122 5.9.2 Depth calibration 124 Chapter 6 Weiner deconvolution by deep learning 127 6.1 Overview 127 6.2 Advantages of Deep Weiner Deconvolution over Conventional Weiner Deconvolution for Signal Recovery 127 6.3 Weiner deconvolution using deep learning 130 6.3.1 Preparation of dataset 130 6.3.2 Weiner deconvolution by deep learning 132 6.3.3 Size of Input spectrum, PSF and Output spectrum 134 6.3.4 Hyperparameters 136 6.3.5 Deconvolution results on testing data 139 Chapter 7 Measurement Results and Analyses 142 7.1 Overview 142 7.1 Light intensity 142 7.2 Tilt measurement of the object space 143 7.3 Polynomial fitting equation 144 7.4 Sample measurement 147 7.4.1 Step height measurement 147 7.4.2 WLI for standard measurement of step height 156 7.4.3 Grating structure 157 7.4.4 AFM and Keyence confocal for standard measurement of the grating structure 161 7.4.5 Ball Grid Array (BGA) sample 165 7.5 Discussions 170 Chapter 8 Conclusions and future work 172 8.1 Conclusions 172 8.2 Future works 174 References 177 | - |
dc.language.iso | en | - |
dc.title | 基於深度學習的維納反捲積方法應用於彩色共焦顯微術之光學解析度提升 | zh_TW |
dc.title | Enhancing Optical Resolution in Chromatic Confocal Microscopy: A Deep Learning-Based Wiener Deconvolution Approach | en |
dc.type | Thesis | - |
dc.date.schoolyear | 111-2 | - |
dc.description.degree | 碩士 | - |
dc.contributor.oralexamcommittee | 章明;葉勝利;李朱育 | zh_TW |
dc.contributor.oralexamcommittee | Ming Chang;Shengli-Li Yeh;Ju-Yi Lee | en |
dc.subject.keyword | 點掃描色差共焦顯微術(PSCCM),深度學習維納解卷積,基於波理論的標量繞射參數模型,實驗評估,微觀特徵,解析度改善, | zh_TW |
dc.subject.keyword | Point Scanning Chromatic Confocal Microscopy (PSCCM),Deep Learning Weiner Deconvolution,Wave Theory-based Scalar Diffraction Parametric Model,Experimental Evaluations,Microscopic Features,Resolution Improvements, | en |
dc.relation.page | 180 | - |
dc.identifier.doi | 10.6342/NTU202304007 | - |
dc.rights.note | 未授權 | - |
dc.date.accepted | 2023-08-14 | - |
dc.contributor.author-college | 工學院 | - |
dc.contributor.author-dept | 機械工程學系 | - |
顯示於系所單位: | 機械工程學系 |
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