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| ???org.dspace.app.webui.jsptag.ItemTag.dcfield??? | Value | Language |
|---|---|---|
| dc.contributor.advisor | 陳正剛 | |
| dc.contributor.author | Ming-Chun Wu | en |
| dc.contributor.author | 吳明俊 | zh_TW |
| dc.date.accessioned | 2021-06-12T17:57:27Z | - |
| dc.date.available | 2008-02-18 | |
| dc.date.copyright | 2008-02-18 | |
| dc.date.issued | 2008 | |
| dc.date.submitted | 2008-01-30 | |
| dc.identifier.citation | Reference
1. Montgomery, D.C., Design and analysis of experiments / Douglas C. Montgomery. 5th ed ed. 2001, New York : John Wiley, 2001: New York : John Wiley, 2001. 2. Atkinson, A.C., Optimum experimental designs / A.C. Atkinson and A.N. Donev. 1992, Oxford [England] : Clarendon Press ; New York : Oxford University Press, 1992: Oxford [England] : Clarendon Press ; New York : Oxford University Press, 1992. 3. Sen, A.K., Regression analysis : theory, methods and applications / Ashish Sen, Muni Srivastava. 1990, New York : Springer-Verlag, c1990: New York : Springer-Verlag, c1990. 4. Kiefer, J., Optimum Experimental Designs. Journal of the Royal Statistical Society, 1959. 21(2): p. 272-319. 5. 簡禎富等, 半導體製造技術與管理. 2005: 新竹市 國立清華大學出版社 民94[2005]. 6. Mizuno, F., Evaluation of total uncertainty in the dimension measurements using critical-dimension measurement scanning electron microscopes. Journal of Vacuum Science and Technology B: Microelectronics Processing and Phenomena, 1998. 16(6): p. 3661-3667. 7. Vladimir, A.U., Effect of bias variation on total uncertainty of CD measurements, J.H. Daniel, Editor. 2003, SPIE. p. 644-650. 8. McNamee, R., Confounding and confounders. Occupational and Environmental Medicine, 2003. 60(3): p. 227-234. 9. S. Greenland and H. Morgenstern, Confounding in health research, in Annual Review of Public Health. 2001. p. 189-212. 10. Meyer, R.K., The Coordinate-Exchange Algorithm for Constructing Exact Optimal Experimental Designs. Technometrics, 1995. 37(1): p. 60-69. 11. John Neter, M.H.K., Christopher J. Nachtsheim, and William Wasserman, Applied linear statistical models / John Neter ... [et al.]. 4th ed ed. 1996, Boston, Mass. : WCB/McGraw-Hill, c1996: Boston, Mass. : WCB/McGraw-Hill, c1996. 12. Johnson, R.A., Applied multivariate statistical analysis / Richard A. Johnson, Dean W. Wichern. 5th ed ed. 2002, Upper Saddle River, N.J. : Prentice Hall, c2002: Upper Saddle River, N.J. : Prentice Hall, c2002. 13. 陳順宇, 多變量分析. 二版 ed. 2000: 臺南市 陳順宇發行 華泰書局總經銷 民90[2000]. 14. Smith, K., On the Standard Deviations of Adjusted and Interpolated Values of an Observed Polynomial Function and its Constants and the Guidance they give Towards a Proper Choice of the Distribution of Observations. Biometrika, 1918. 12(1/2): p. 1-85. 15. Cook, R.D., On the Equivalence of Constrained and Compound Optimal Designs. Journal of the American Statistical Association, 1994. 89(426): p. 687-692. 16. Huang YC, W.W., Sequential construction of multiple-objective optimal designs. Biometrics., 1998. 54(4): p. 1388-1397. 17. Kee Wong, W.K., Recent advances in multiple-objective design strategies. Statistica Neerlandica, 1999. 53(3): p. 257-276. 18. Casella, G., Statistical inference / George Casella, Roger L. Berger. 2nd ed ed. 2002, Australia ; Pacific Grove, CA : Duxbury/Thomson Learning, c2002: Australia ; Pacific Grove, CA : Duxbury/Thomson Learning, c2002. 19. Atkinson, A.C., D-Optimum Designs for Heteroscedastic Linear Models. Journal of the American Statistical Association, 1995. 90(429): p. 204-212. 20. Vining, G.G., EXPERIMENTAL DESIGNS FOR ESTIMATING BOTH MEAN AND VARIANCE FUNCTIONS. Journal of Quality Technology, 1996. 28(2): p. 135-147. 21. Mukerjee, R., Optimal design for the estimation of variance components. Biometrika, 1988. 75(1): p. 75-80. 22. Davidian, M., Variance Function Estimation. Journal of the American Statistical Association, 1987. 82(400): p. 1079-1091. 23. Box, G.E.P., Draper N. R., Robust designs. Biometrika, 1975. 62(2): p. 347-352. 24. Welch, W.J., Branch-and-Bound Search for Experimental Designs Based on D Optimality and Other Criteria. Technometrics, 1982. 24(1): p. 41-48. 25. Haines, L.M., The Application of the Annealing Algorithm to the Construction of Exact Optimal Designs for Linear-Regression Models. Technometrics, 1987. 29(4): p. 439-447. 26. Fedorov, V.V., Theory of optimal experiments 1972, New York : Academic Press, 1972: New York : Academic Press, 1972. 27. Atkinson, A.C., The construction of exact D-optimum experimental designs with application to blocking response surface designs. Biometrika, 1989. 76(3): p. 515-526. 28. Welch, W.J., Computer-Aided Design of Experiments for Response Estimation. Technometrics, 1984. 26(3): p. 217-224. 29. Box, M.J., Draper N. R., Factorial Designs, the |X prime X| Criterion, and Some Related Matters. Technometrics, 1971. 13(4): p. 731-742. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/27187 | - |
| dc.description.abstract | 在製造業中為了保持競爭力，提高產品的良率是必要的；利用實驗設計的方法-最佳化實驗設計可以協助我們找到最大良率的製程因子組合。當反應值(response)須藉由測量才能得知，而且因量測因子的效應(effect)而無法精確的得到時，量測誤差便無法被視為隨機誤差忽略，因為製程因子的效應和量測因子的效應可能會混在一起，這樣的現象稱為混淆(confounding)。我們討論的量測誤差分為兩種情況，一種情況是量測混淆效應只導致反應值有特定的偏移；另一種情況是量測混淆效應導致反應值有異質的變異數(heterosedastic)。以D最佳化實驗設計處理混淆時，除了將量測混淆因子加入模型外，因子和因子的對比之間依然存在著共線性(multicollinearity)；且兩個搜尋3水準階層點的D最佳化實驗設計有相同的目標函數值，卻有不同的相關結構。因此我們希望製程因子和量測因子效應之間的混淆愈小愈好，還必須讓製程因子和量測因子對比的線性相關愈小愈好。
本研究希望能達成的目的有(1)效應的估計愈精確愈好，以及(2)製程因子效應和量測因子效應的混淆最小化的兩個目標。研究的成果顯示，我們提出的多目標最佳化實驗設計能有效的達成上述兩個目的，是傳統的D最佳化實驗設計所無法做到的。 | zh_TW |
| dc.description.provenance | Made available in DSpace on 2021-06-12T17:57:27Z (GMT). No. of bitstreams: 1 ntu-97-R94546026-1.pdf: 693167 bytes, checksum: afbdb88222e5a7c6ca49c55fe6f62fec (MD5) Previous issue date: 2008 | en |
| dc.description.tableofcontents | 目錄
論文摘要 i 表格目錄 v 圖表目錄 vi 1. 簡介 1 1.1 背景和文獻回顧 1 1.2 研究目的及論文架構 7 2. 製程因子影響和量測因子影響混淆的最小化 8 2.1 製程因子影響和量測因子影響的混淆 8 2.2 混淆影響最小化的目標函數 11 2.3 最佳化目標函數之選取 12 3. 最佳化實驗設計 21 3.1 Information number 21 3.2 加法模型 22 3.2.1 D-最佳化實驗設計 22 3.2.2 Ds-最佳化實驗設計 23 3.3 異質變異數的線性模型 25 3.3.1 Information matrix和目標函數 25 3.3.2 包含參數的information matrix 27 3.4 多目標最佳化實驗設計 28 3.4.1 簡介 28 3.4.1混淆影響管制下之D-實驗設計最佳化 29 3.4.2混淆影響管制下之Ds-實驗設計最佳化 31 4 提出的多目標實驗設計的績效 32 4.1 演算法 32 4.2 展示與比較 34 5 結論 54 Reference 55 附錄1 Score function的期望值 59 附錄2 証明 60 附錄3 Information number的性質 60 附錄4 異質變異數的information matrix 62 附錄5 說明 和限制式(constrain)之間的關係 64 | |
| dc.language.iso | zh-TW | |
| dc.subject | 混淆 | zh_TW |
| dc.subject | 異質變異數線性模型 | zh_TW |
| dc.subject | 最佳化實驗設計 | zh_TW |
| dc.subject | confounding | en |
| dc.subject | heteroscedastic linear model | en |
| dc.subject | optimum design | en |
| dc.title | 考慮量測混淆效應之實驗設計最佳化 | zh_TW |
| dc.title | Optimization experimental design subject to confounding measurement effects | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 96-1 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 黃榮臣,廖振鐸 | |
| dc.subject.keyword | 混淆,最佳化實驗設計,異質變異數線性模型, | zh_TW |
| dc.subject.keyword | confounding,optimum design,heteroscedastic linear model, | en |
| dc.relation.page | 54 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2008-01-30 | |
| dc.contributor.author-college | 工學院 | zh_TW |
| dc.contributor.author-dept | 工業工程學研究所 | zh_TW |
| Appears in Collections: | 工業工程學研究所 | |
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| File | Size | Format | |
|---|---|---|---|
| ntu-97-1.pdf Restricted Access | 676.92 kB | Adobe PDF |
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