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http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/83863完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 顏家鈺(Jia-Yush Yen) | |
| dc.contributor.author | Chen-Yu Chen | en |
| dc.contributor.author | 陳鎮宇 | zh_TW |
| dc.date.accessioned | 2023-03-19T21:21:09Z | - |
| dc.date.copyright | 2022-07-26 | |
| dc.date.issued | 2022 | |
| dc.date.submitted | 2022-07-22 | |
| dc.identifier.citation | 1. J. Pan, Z. Hu, Z. Su, and M.-H. Yang, 'Deblurring text images via L0-regularized intensity and gradient prior,' IEEE Conference on Computer Vision and Pattern Recognition, pp. 2901-2908, 2014, doi: 10.1109/CVPR.2014.371. 2. Robert D. Fiete, 'Modeling the Imaging Chain of Digital Cameras,' Vol. TT92. 3. Ioannis Gkioulekas, 'Computational Photography, Carnegie Mellon University,' 2019. 4. David Voelz, 'Computational Fourier Optics A MATLAB Tutorial,' Vol.TT89. 5. Qiu, Zhi-Yong, 'Deblurring Process of Blurred Image for Dynamic Photography,' 2016. 6. William Richardson, 'Bayesian-Based Iterative Method of Image Restoration,' Journal of the Optical Society of America, vol. 62, no. 1, pp. 55-59, 1972, doi: https://doi.org/10.1364/JOSA.62.000055. 7. MM Sondhi, 'Image restoration: The removal of spatially invariant degradations,' Proceedings of the IEEE, vol. 60, no. 7, pp. 842-853, 1972, doi: 10.1109/PROC.1972.8783. 8. Rob Fergus, Barun Singh, Aaron Hertzmann, Sam T. Roweis, and William T. Freeman, 'Removing Camera Shake from a Single Photograph,' ACM Transactions on Graphics, vol. 25, no. 3, pp. 787-794, 2006, doi: https://doi.org/10.1145/1141911.1141956. 9. Oliver Whyte, Josef Sivic, Andrew Zisserman, and Jean Ponce, 'Non-uniform Deblurring for Shaken Images,' IEEE Conference on Computer Vision and Pattern Recognition, 2010, doi: 10.1109/CVPR.2010.5540175. 10. Xiangyu Xu, Jinshan Pan, Yu-Jin Zhang, and Ming-Hsuan Yang, 'Motion Blur Kernel Estimation via Deep Learning,' IEEE Transactions on Imaging Processing, vol. 27, no. 1, pp. 194-205, 2018, doi: 10.1109/TIP.2017.2753658. 11. Orest Kupyn, Tetiana Martyniuk, Junru Wu, and Zhangyang Wang, 'DeblurGAN-v2: Deblurring (Orders-of-Magnitude) Faster and Better,' International Conference on Computer Vision, pp. 8877-8886, 2019, doi: 10.1109/ICCV.2019.00897. 12. Huaijin Chen, Jinwei Gu, Orazio Gallo, Ming-Yu Liu, Ashok Veeraraghavan, and Jan Kautz, 'Reblur2Deblur: Deblurring Videos via Self-Supervised Learning,' International Conference on Computational Photography, 2018, doi: https://doi.org/10.48550/arXiv.1801.05117. 13. Seungjun Nah, Sanghyun Son, and Kyoung Mu Lee, 'Recurrent Neural Networks with Intra-Frame Iterations for Video Deblurring,' Computer Vision and Pattern Recognition, 2015, doi: 10.1109/CVPR.2019.00829. 14. USAF-11951 Linear Direct Read Resolution Target, Available from: https://www.appliedimage.com (accessed. 15. Structural Similarity, Available from: https://en.wikipedia.org/wiki/Structural_similarity (accessed. 16. Camera Datasheet, Available from: https://www.1stvision.com/cameras/models/IDS-Imaging/UI-2230SE (accessed. 17. Min Wei, 'Control and Trajectory Accuracy Research for a Large Stroke Wafer Inspection Stage,' 2018. 18. Ndrive HLe, Available from: https://www.aerotech.com/product/motion-control-platforms/ndrive-hle-high-performance-linear-digital-drive/ (accessed. 19. Ndrive ML, Available from: https://www.aerotech.com/product/motion-control-platforms/ndrive-ml-high-performance-linear-digital-drive/ (accessed. 20. Linear Motor, Available from: https://en.wikipedia.org/wiki/Linear_motor (accessed. 21. Kennedy, J., and R. Eberhart, 'Particle Swarm Optimization,' Proceedings of the IEEE International Conference on Neural Network, pp. 1942–1945, 1995. 22. Marco Bevilacqua, Aline Roumy, Christine Guillemot, Marie-Line Alberi Morel, 'Low-Complexity Single-Image Super-Resolution based on Nonnegative Neighbor Embedding,' British Machine Vision Conference, 2012, doi: 10.5244/C.26.135. 23. Marco Bevilacqua, Aline Roumy, Christine Guillemot, Marie-Line Alberi Morel, 'Low-Complexity Single-Image Super-Resolution based on Nonnegative Neighbor Embedding,' British Machine Vision Conference, 2012, doi: 10.5244/C.26.135. 24. Yudong Liang, Ze Yang, Kai Zhang, Yihui He, Jinjun Wang, Nanning Zheng, 'Single Image Super-resolution via a Lightweight Residual Convolutional Neural Network', Computer Vision and Pattern Recognition, 2017, doi: https://doi.org/10.48550/arXiv.1703.08173. 25. Jonathan D. Simpkins, 'Modeling and Estimation of Spatially-varying Point-spread-functions Due to Lens Aberrations and Defocus,' 2011. 26. Gregory Hollows, and Nicholas James, 'The Airy Disk and Diffraction Limit,' Available from: https://www.edmundoptics.com.tw/knowledge-center/application-notes/imaging/limitations-on-resolution-and-contrast-the-airy-disk/ (accessed. 27. Gergely Vass, and Tam?s Perlaki, 'Applying and Removing Lens Distortion in Post Production,' 2003. 28. Ian Norman, 'A Practical Guide to Lens Aberrations and the Lonely Speck Aberration Test,' Available from: https://www.lonelyspeck.com/a-practical-guide-to-lens-aberrations-and-the-lonely-speck-aberration-test/ (accessed. 29. L. Xu, C. Lu, Y. Xu, and J. Jia, 'Image smoothing via L0 gradient minimization,' ACM Transactions on Graphics, vol. 30, no. 6, pp. 1-12, 2011, doi: https://doi.org/10.1145/2070781.2024208. 30. Gamma Correction, Available from: https://www.mathworks.com/help/images/gamma-correction.html (accessed. 31. Reui-Yang Yu, 'Precision 2D Servo Design for a Large Stroke Wafer Coplanar Inspection Stage,' 2017. 32. Jie-An Chen, 'Precision Servo Design Based upon Artificial Intelligent Algorithm,' 2019. 33. Syuan-You Jhu, 'Highly Steady Trajectory and Speed Control of an Ultra-Precision Wafer Inspection Stage,' 2020. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/83863 | - |
| dc.description.abstract | 在半導體製程中,晶圓檢測的技術至關重要,在維持高精度的前提下,提升檢測平台的掃描速度有助於降低時間成本。然而,掃描速度的提升也意味著拍攝的影像可能出現動態模糊,本論文試圖從主動控制的角度,還原遭受動態模糊破壞的影像。 首先,將全域式快門相機架設至平台後,控制平台等速移動以拍攝動態模糊的影像,接著,對模糊影像進行光學變形校正,再使用Pan所提出的影像還原方法,還原得到清晰的影像。Pan的影像還原模型,包含影像灰階值和影像梯度資訊,其演算法是利用迭代的方式,從粗糙到精細輪流求出模糊核和潛在影像。 使用解析度測試圖作為拍攝對象,經由影像還原模型去除模糊後,可針對結果繪製MTF圖表,本論文設計多種實驗條件,以比較影像還原的成果。除了發展高速影像還原的技術,本論文也對晶圓檢測平台的馬達進行系統識別,再根據識別結果設計控制器,以降低平台移動的位置誤差。 | zh_TW |
| dc.description.abstract | Wafer inspection technology has its greatest importance in the semiconductor manufacturing. Under the condition of maintaining high precision, increasing the inspection stage’s scanning speed contributes to time and cost savings. However, the increase of scanning speed also implies that there might exist motion blur in captured images. By controlling the imaging setup, this thesis tries to recover motion-blur-degraded images. Firstly, the global shutter camera is installed on the stage, and the stage is controlled to move constantly in order to capture motion-blurred images. Secondly, the optical distortion correction is applied to the blurred image, and Pan’s image deblurring method is adopted to get the clear image. Pan’s image deblurring model includes image pixel values and image gradients information. In a coarse-to-fine manner, the algorithm iteratively solves the blur kernel and the latent image in turns. By selecting the resolution test chart and using the image deblurring model to remove the blur, modulation transfer function can be drawn from the result. This thesis designs several experimental conditions and compares their deblurred results. In addition to developing image restoration techniques, this thesis identifies motors’ system of the wafer inspection stage, and designs the controller based on the result to reduce the stage’s position error in motion. | en |
| dc.description.provenance | Made available in DSpace on 2023-03-19T21:21:09Z (GMT). No. of bitstreams: 1 U0001-2207202215115800.pdf: 24624614 bytes, checksum: a7d4dee76369ae890a4147cef4e05337 (MD5) Previous issue date: 2022 | en |
| dc.description.tableofcontents | 第1章 導論 1 1.1 研究背景與動機 1 1.2 論文架構 1 第2章 文獻探討 3 2.1 數位影像模型 3 2.2 光繞射與光學解析度 4 2.3 像差 6 2.4 動態模糊還原 8 第3章 影像還原演算法 11 3.1 演算法概述 11 3.2 光學變形校正 12 3.3 動態模糊還原數學模型 13 3.3.1 影像先驗資訊 14 3.3.2 模糊影像還原模型 15 3.4 動態模糊還原演算法 17 3.4.1 影像金字塔 17 3.4.2 潛在清晰影像演算法 19 3.4.3 最終清晰影像演算法 19 3.5 影像清晰度分析 20 第4章 系統架構介紹 23 4.1 影像還原實驗設備 23 4.2 晶圓檢測平台 25 4.2.1 硬體架構 26 4.2.2 線性馬達 27 4.2.3 驅動器 28 4.2.4 位置量測系統 29 第5章 平台系統識別與控制器設計 30 5.1 線性馬達數學模型 30 5.2 系統識別 33 5.2.1 馬達訊號量測 34 5.2.2 二階諧振模型 34 5.2.3 成本函數設計、尋找參數之演算法 36 5.2.4 X軸馬達識別結果 37 5.2.5 Y軸馬達識別結果 38 5.3 控制器設計 39 5.3.1 伺服控制架構 39 5.3.2 X軸控制模擬結果 43 5.3.3 Y軸控制模擬結果 44 第6章 實驗結果 46 6.1 影像還原過程解析 46 6.1.1 灰階資訊對影像還原結果之影響 50 6.1.2 灰階梯度資訊對影像還原結果之影響 50 6.2 平台移動速度對影像還原結果之影響 54 6.3 模糊影像亮度對影像還原結果之影響 61 6.4 不同拍攝物體之影像還原結果 66 6.4.1 分辨力測試圖正片轉45度角 66 6.4.2 分辨力測試圖負片 68 6.4.3 灰階測試圖 69 第7章 結論與未來展望 70 7.1 結論 70 7.2 未來展望 72 7.2.1 超解析度成像 72 7.2.2 線掃描相機 75 7.2.3 其他研究方向 75 第8章 參考資料 77 | |
| dc.language.iso | zh-TW | |
| dc.subject | 高速掃描 | zh_TW |
| dc.subject | 影像還原 | zh_TW |
| dc.subject | 動態模糊 | zh_TW |
| dc.subject | High-speed scanning | en |
| dc.subject | Motion blur | en |
| dc.subject | Image restoration | en |
| dc.title | 藉高速拍攝輔助晶圓檢測系統研究 | zh_TW |
| dc.title | Research on High-Speed Imaging Assisted Wafer Scanning System | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 110-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 陳亮嘉(Liang-Chia Chen),何昭慶(Chao-Ching Ho) | |
| dc.subject.keyword | 高速掃描,動態模糊,影像還原, | zh_TW |
| dc.subject.keyword | High-speed scanning,Motion blur,Image restoration, | en |
| dc.relation.page | 80 | |
| dc.identifier.doi | 10.6342/NTU202201644 | |
| dc.rights.note | 未授權 | |
| dc.date.accepted | 2022-07-22 | |
| dc.contributor.author-college | 工學院 | zh_TW |
| dc.contributor.author-dept | 機械工程學研究所 | zh_TW |
| 顯示於系所單位: | 機械工程學系 | |
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