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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 陳中平(Chung-Ping Chen) | |
dc.contributor.author | Kuan-Lu Huang | en |
dc.contributor.author | 黃冠儒 | zh_TW |
dc.date.accessioned | 2021-05-20T20:00:31Z | - |
dc.date.available | 2011-02-11 | |
dc.date.available | 2021-05-20T20:00:31Z | - |
dc.date.copyright | 2010-02-11 | |
dc.date.issued | 2010 | |
dc.date.submitted | 2010-02-03 | |
dc.identifier.citation | [1] X. Niu, N. Jakatdar, J. Bao, and C. J. Spanos, “Specular spectroscopic scatterometry,” IEEE Trans. Semicond. Manuf. 14, 97–111 (2001).
[2] R. Antos, J. Pistora, I. Ohlidal, K. Postava, J. Mistrik, T. Yamaguchi, S. Visnovsky, and M. Horie, “Specular spectroscopic ellipsometry for the critical dimension monitoring of gratings fabricated on a thick transparent plate,” J. Appl. Phys. 97, 053107 (2005). [3] H. Huang and F. L. Terry, “Spectroscopic ellipsometry and reflectometry from gratings (scatterometry) for critical dimension measurement and in situ, real-time process monitoring,” Thin Solid Films 455, 828–836 (2004). [4] C. J. Raymond, M. R. Murnane, S. S. H. Naqvi, and J. R. McNeil, “Metrology of subwavelength photoresist gratingsusing optical scatterometry,” J. Vac. Sci. Technol. B 13, 1484–1495 (1995). [5] J. Garnaes, P. E. Hansen, N. Agersnap, J. Holm, F. Borsetto, and A. Kühle, “Profiles of a high-aspect-ratio grating determined by spectroscopic scatterometry and atomic-force microscopy,” Appl. Opt. 45, 3201–3212 (2006). [6] R. H. Krukar, S. L. Prins, D. M. Krukar, G. A. Petersen, S. M. GasparGaspar, J. R. McNeil, S. S. H. Naqvi, and D. R. Hush, “Using scattered light modeling for semiconductor critical dimension metrology and calibration,” Proc. SPIE 1926, 60–71 (1993). [7] I. Kallioniemi, J. Saarinen, and E. Oja, “Optical scatterometry of subwavelength diffraction gratings: neural network approach,” Appl. Opt. 37, 5830–5835 (1998). [8] S. Robert, A. M. Ravaud, S. Reynaud, S. Fourment, F. Carcenac, and P. Arguel, “Experimental characterization of subwavelength diffraction gratings by an inverse-scattering neural method,” J. Opt. Soc. Am. A 19, 2394–2402 (2002) [9] http://www.rsoftdesign.com [10] Max Born and Emil Wolf. Principle of Optics. Press Syndicate of The University of Cambridge, 7 edition, 2005. pp.569-572. [11] Max Born and Emil Wolf. Principle of Optics. Press Syndicate of The University of Cambridge, 7 edition, 2005. [12] A. K. Wong. Optical Imaging in Projection Microlithography. SPIE Press, 2005 [13] H.H. Hopkins. “The Concept of Partial Coherence in Optics.” In Proc. Royal Soc.London,A208:263-277,1951 [14] Ming-Feng Tsai 'Abbe-PCA:Compact Abbe's Kernel Generation for Microlithography Aerial Imags Simulation using Principal Compoents Analysis',National Taiwan University [15] Szu-Kai Lin 'An Efficient Contour Generation Algorithm for Microlithograhy Aerial Image', Naitional Taiwan University [16] Jui-Hsiang Liu, Kuan-Lu huang, Tsung-Yu Li, Meng-Chun Chiu, Charlie Chung-Ping Chen Chih-Sheng Jao, Lon Wang 'Efficient and Accurate Optical Scatterometry Diagnosis of Grating Variation Based on Segmented Moment Matching and Singular Value Decomposition Method',Graduate Insitute of Photonics and Optoelectronics Enginerring ,National Taiwan University [17] NVIDIA(2008).'CUDA Programming Guide v2.0' [18] Steven Cook (2007). 'Examining Suitability of Multicore Processor Architectures for Solving Realistic Computationally Intensive Problems by Simulating Synaptic Behavior in Large Neural Networks'. [19] Wikipedia(2009). Tesla, http://en.wikipedia.org/wiki/NVIDIA_Tesla [20] 林壽佑(2008) A Preliminary Study of Using Graphic Processors on Discrete Element Method Computation 國立台灣科技大學營建工程系 [21] J. Breitbart CuPP -- A framework for easy CUDA integration, HiPS 2009 workshop with IPDPS 2009, Rome, Italy, May 2009.. [22] J. Breitbart: Case studies on GPU usage and data structure design, Master Thesis, University of Kassel, 2008. [23 ] http://en.wikipedia.org/wiki/Central_processing_unit#History | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/8742 | - |
dc.description.abstract | 為了保證奈米壓印製造光柵的品質,光散(opticalscatterometry) 是一個有效率和有效的方法來診斷實際光柵的幾何形狀。為了方便診斷的過程,一個有效率針對大型資料庫的匹配演算法是非常重要的。在本篇論文中,我們提出一個有效的演算法利用最小誤差(MSE)的方式用來比對大型的頻譜資料庫,藉此反推原始的幾何組態。我們利用奇異值分解(Singular Value Decomposition)對大型的資料庫作壓縮並使用分層的動差(Moment)匹配方式來執行匹配演算法。我們的搜尋和診斷演算法是非常快速且精確的。跟傳統的最小誤差比起來,快上了3000倍以上且精確度在0.1%以內。
第二部分是介紹使用平行計算的方式來加快微顯影中的成像生成。隨者超大型積體電路技術的特徵尺寸(feature size)迅速縮小,已小於曝光光的的波長,光的繞射效應使得曝光後的圖像明顯偏離了原本設計的光罩。因此,微顯影結果的品質,在超大型積體電路(VLSI)的製造過程中是非常重要的。但是往往花費了相當多的時間來產生成像。在論文中,我們使用CUDA技術,它是一個通用的平行計算架構,充分利用在NVIDIA繪圖晶片(GPU)中的平行計算引擎,用來加快微顯影中的圖像生成。 | zh_TW |
dc.description.abstract | To ensure the quality of the nanoprint fabricated optical gratings, optical scatterometry (OS) is an efficient and effective mean to diagnose the actual fabricated geometry. To facilitate the diagnosis process, efficient pattern matching algorithms over a huge database are of great importance.
In this thesis, we propose an efficient algorithm using minimum error square approach used to matching in a huge simulated spectrum database in order to obtain the original geometric configuration inversely.We use Singular Value Decomposition to do compression on large database and the use of hierarchical moment to perform matching algorithm; our searching and diagnosis algorithm is extremely fast and accurate. It is over 3000x faster than a exhausted searching algorithm within 0.1% accuracy. The second part is to introduce the use of parallel computing in the imaging of microlithography for acceleration. As the VLSI technology feature sizes quickly shrink smaller than the wavelength of exposure light sources, the diffraction effects have made the exposed patterns significantly deviated from the original intended mask pattern. Therefore, the quality of microlithography simulation is an important part of the VLSI manufacturing process. However, it takes considerable time to produce image. In the thesis, we use CUDA, which is a general purpose parallel computing architecture that leverages the parallel compute engine in NVIDIA graphics processing units (GPUs) to speed up the image generation in Microlithography simulation. | en |
dc.description.provenance | Made available in DSpace on 2021-05-20T20:00:31Z (GMT). No. of bitstreams: 1 ntu-99-R96943021-1.pdf: 1896323 bytes, checksum: 182c7a37ae95815be46aea9ea9c6e401 (MD5) Previous issue date: 2010 | en |
dc.description.tableofcontents | 中文口試委員審定書 i
英文口試委員審定書 ii 致謝 iii 摘要 iv Abstract v Content 1 List of Figures 4 List of Tables 6 Chapter 1. Efficient Ways for Optical Scatterometry Diagnosis 7 1.1. Introduction and Background 7 1.2. Previous Work 8 1.3. Motivation 9 1.4. Spectrum Diagnosis Scheme 10 1.4.1. Database Construction 11 1.4.2. Problem Formulation 12 1.4.3. Eigenvalue Decomposition 13 1.4.4. Compaction by Singular Value Decomposition 15 1.4.5. Classification 18 1.4.6. Segmented Moment Matching 21 1.4.7. Grid-based Searching 27 1.5. Approach to Arbitrary Segment Spectrum Range 32 1.6. Simulation Results 34 1.7. Runtime Comparison 39 1.8. Summary 40 1.9. Feature Work 40 Chapter 2. Parallel Optical Simulation Using Graphic Processor Unit 41 2.1. Introduction and Background 41 2.2. Motivation 41 2.3. Preliminary 42 2.4. Overview of Image Generation 43 2.4.1. Imaging Equation 43 2.4.2. The Abbe’s Method 45 2.4.3. Mask Decomposition 48 2.4.4. Lookup Table 49 2.4.5. Image Generation Flow 53 2.4.6. Mask Pattern Partition 54 2.5. Parallel Optical Simulation 55 2.5.1. Code Analysis 56 2.5.2. The Evolution of Microprocessor 58 2.5.3. Multi-core Background Overview 58 2.5.4. NVIDIA CUDA 60 2.5.5. Architecture of Tesla C1060 61 2.5.6. Software Model 64 2.5.7. Approach to Parallel Lookup Table 67 2.5.8. Kernel Execution Flow 68 2.5.9. The Optimization on Our Work 69 2.6. Performance Comparison 72 2.6.1. Grid size and Block size 73 2.6.2. Execution Time Comparison 74 2.6.3. Transmission Overhead 77 2.6.4. Relative Error 78 2.7. Summary 79 2.8. Feature work 80 Bibliography 81 Appendix A 85 | |
dc.language.iso | en | |
dc.title | 頻譜診斷與微顯影平行計算 | zh_TW |
dc.title | Spectrum Diagnosis and Parallel Optical Simulation | en |
dc.type | Thesis | |
dc.date.schoolyear | 98-1 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 蔡坤諭,王倫,洪士灝(Shih-Hao Hung) | |
dc.subject.keyword | 光散射,奇異值分解,動差匹配,阿貝成像方法,繪圖處理器平行運算, | zh_TW |
dc.subject.keyword | Optical Scatterometry, Singular Value Decomposition,Moment Matching,Abbe’s method,CUDA, | en |
dc.relation.page | 85 | |
dc.rights.note | 同意授權(全球公開) | |
dc.date.accepted | 2010-02-03 | |
dc.contributor.author-college | 電機資訊學院 | zh_TW |
dc.contributor.author-dept | 電子工程學研究所 | zh_TW |
顯示於系所單位: | 電子工程學研究所 |
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