跳到主要內容

臺灣博碩士論文加值系統

(44.201.97.224) 您好!臺灣時間:2024/04/14 20:12
字體大小: 字級放大   字級縮小   預設字形  
回查詢結果 :::

詳目顯示

我願授權國圖
: 
twitterline
研究生:廖德銘
研究生(外文):Dirac Liao
論文名稱:光耦合器產品品質之發展
論文名稱(外文):The Product Quality Development of Fiber Coupler
指導教授:蘇朝墩蘇朝墩引用關係
指導教授(外文):C. T. Su
學位類別:博士
校院名稱:國立交通大學
系所名稱:工業工程與管理系
學門:工程學門
學類:工業工程學類
論文種類:學術論文
論文出版年:2003
畢業學年度:91
語文別:中文
論文頁數:94
中文關鍵詞:光纖耦合器可靠度田口方法多重反應問題類神經網路基因法則演算法指數滿意度函數
外文關鍵詞:fiber couplerreliabilityTaguchi methodmulti-response problemneural networkgenetic algorithmexponential desirability function
相關次數:
  • 被引用被引用:8
  • 點閱點閱:291
  • 評分評分:
  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:3
摘 要
全球光通信傳輸所面臨的困難是各傳輸網所需的基礎模組與元件的規格不統一。在眾多光被動元件中之光耦合器製造,因牽涉到光學、機械、電子、熱融等物理的控制,尚無法自動化,量產所必須面臨的管理與技術整合問題頗多,其中產品品質的發展是一項重要的挑戰課題。本文探討並介紹光耦合器產品品質之發展,建議藉由產品設計驗證與製程參數最佳化兩項產品品質發展工具,確保光耦合器的產品品質與生產良率改善。本文所研究的結果可提供類似光通信元件廠,欲建立具品質與成本競爭優勢的光耦合器產品的參考。
本文所建議的產品品質發展工具包含:產品研發可靠度設計驗證,製程參數最佳化。前者確保產品的可靠度能符合市場的規範要求;後者深入探討關鍵燒拉製程參數最佳化,應用(A)田口方法,(B)類神經網路與指數滿意度函數分析與(C)類神經網路、指數滿意度函數與基因法則演算法之整合分析等三項常用的方法來提昇製程良率,所建立的參數最佳化程序可取代傳統的嘗試錯誤方法,迅速滿足光通信市場上大量客製化規格需求的製程參數。總結來說,第三種整合方法最適宜用來改善燒拉製程參數最佳化。
最後,本文以新竹科學園區某光通信被動元件公司為例,說明在光通信元件產業中應用本文產品品質之發展的成效。初期以可靠度設計驗證來提昇其產品本身光特性與品質的優越性,順利完成各項嚴厲的環境試驗挑戰,不過產品燒拉製程良率僅能達成65%左右。接著,進行第二階段燒拉製程的參數最佳化,使產品的製程良率由65%改善至平均99%以上,且特優等級產品佔90%以上。本文光耦合器產品品質之發展,不僅僅獲得上述成果,對客製化產品的評估反應時間也從七至十天縮短為一至二天內完成,各種結果均顯現出令人滿意的結論。
ABSTRACT
The worldwide difficulties in fiber optical transmission industry these years are coming from various developing techniques and inconsistent specifications to the basic devices and components. Among numerous passive devices, the fused fiber coupler manufacturing is most difficult to be fully automated due to its physical interactions among optics, mechanics, electrics, and heat fusion. The product quality development of fiber coupler is a critical and challenging task. This study illustrates the progress of fiber coupler quality development and recommends two tools, reliability design verification and fusion process optimization approach, to ensure the product and process quality. The success of the implementation could be provided to those manufacturers as a useful reference to establish the coupler product with competitive advantages.
The two tools for ensuring fiber coupler quality are reliability design verification and fusion process optimization approach. The former provides the conformity of the product by meeting the requirements in optical industry; the latter provides the competitive cost performance by significant improvement in production yield rate. This dissertation also deeply illustrates the procedures to optimize fiber coupler fusion parameters with three different approaches, i.e. (a) Taguchi methods, (b) neural network and exponential desirability function analysis and (c) integrated neural network, exponential desirability function and genetic algorithm analysis. This study demonstrates it could easily take place of traditional trial-and-error approach, to promptly fulfill the need of frequent specification changes from market. In summary, the outcomes from the third hybrid approach are superior to the first two approaches and it is also very suitable to solve the problems for parameter optimization in fusion process.
Finally, this product quality development of fiber coupler is successfully demonstrated on a commercial passive optical communication company in Hsinchu Science-based Industrial Park, Taiwan. The first illustration demonstrates product reliability by design verification. The result is encouraging that low optical performance variations has been observed under severe environmental testing as specified in optical industry. But it is unsatisfied that only 65% fusion process yielding ratio was achieved. The second illustration demonstrates process quality by parameters optimization. The recommended hybrid approach, (c), demonstrates that the exercised company not only obtain considerable yielding ratio improvement in fiber coupler fusion process from 65% to 99% but also gain supreme product performance and quality. In addition to above creditable advantages, the cycle time of specification evaluation for customized product has been reduced from 7~10 days to 1~2 days. All above evidences conclude to satisfactory results.
第一章 緒論
1.1 研究背景
1.2 研究動機
1.3 研究目的
1.4 研究範圍與限制
1.5 論文架構
第二章 光耦合器產品簡介
2.1 光耦合器的分類
2.2 光耦合器的製作流程
2.3 光耦合器的主要光學特性與定義
2.4 熔融拉錐法製造技術
2.5 光耦合器產品品質構面
第三章 文獻探討
3.1 田口方法
3.2 多重反應分析
3.3 指數滿意度函數
3.4 類神經網路
3.4.1 倒傳遞類神經網路
3.5 基因演算法則
第四章 光耦合器產品品質發展模型
4.1 光耦合器產品品質之設計驗證符合性
4.2 光耦合器製程品質之燒拉參數最佳化
4.2.1 田口方法
4.2.2 類神經網路與指數滿意度函數分析法
4.2.3 類神經網路、指數滿意度函數與基因法則演算法之分析法
第五章 光耦合器產品品質案例分析
5.1 光耦合器可靠度驗證與失效分析矯正
5.2 燒拉製程參數最佳化案例分析
5.2.1 品質特性、實驗規劃與數據蒐集
5.2.2 應用田口方法進行分析
5.2.3 應用類神經網路與指數滿意度函數進行分析
5.2.4 應用類神經網路、指數滿意度函數與基因法則演算法進行分析
5.3 結果比較
第六章 結論與展望
6.1 結論
6.2 展望
1. Antony, J. 2001, “Simultaneous Optimization of Multiple Quality Characteristics in Manufacturing Process Using Taguchi’s Quality Loss”, International Journal of Advanced Manufacturing Technology, 17, pp.134-138.
2. Brameier, M. and Banzhaf, W., 2001, “A Comparison of Linear Genetic Programming and Neural Networks in Medical Data Mining”, IEEE Transactions on Evolutionary Computation, 5, 1, pp.17-26.
3. Bright, M. S. and Arslan, T., 2001, “Synthesis of Low-Power DSP Systems Using a Genetic Algorithm”, IEEE Transactions on Evolutionary Computation, 5, 1, pp.27-40.
4. Busacca, P. Giuggioli, Marseguerra, M. and Zio, E., 2001, “Multiobjective Optimization by Genetic Algorithm: Application to Safety Systems”, Reliability Engineering and System Safety, 72, pp.59-74.
5. Cornell, J. A. and Khuri, A. I., 1987, Response Surface: Designs and Analysis, New York: Marcel Dekker.
6. Das, A. K. and Pandit, M. K., 1990, “Analysis and Modeling of Low Loss Fused Fiber Couplers”, Proceedings of SPIE, No.1365, pp.74-85.
7. Das, A. K. and Rakshit, B., 1989, “Optical Fiber Couplers”, Proceedings of SPIE, No.1176, pp.48-56.
8. Das, P., 1999, “Concurrent Optimization of Multiresponse Product Performance”, Quality Engineering, 11, pp.365-368.
9. Dengiz, B., Altiparmak, F. and Smith, A., 1997 “Local Search Genetic Algorithm for Optimization of Highly Reliable Communication Networks”, Proceedings of the seventh International Conference on Genetic Algorithms, ICGA''97, Michigan State University, pp. 650-657
10.Derringer, G. and Suich, R., 1980, “Simultaneous Optimization of Several Response Variables”, Journal of Quality Technology, 12, pp.214-219.
11.Fausett, L., 1994, Fundamentals of Neural Networks: Architecture, Algorithms, and Applications, New Jersey, Prentice-Hall.
12.Fujita, O., 1998, “Statistical Estimation of the Number of Hidden Units for Feedforward Neural Networks”, Neural Networks, 11, pp.851-859.
13.Funahashi, K., 1989, “On the Approximate Realization of Continuous Mapping by Neural Networks”, Neural Networks, 2, pp.18-192.
14.Gao, L. S., Shen, G. and Wang, S. C., 2000, “Intelligent Scheduling Model and Algorithm for Manufacturing”, Production Planning & Control, 11, pp.234-243.
15.Goldberg, D. E., 1989, Genetic Algorithm in Search, Optimization and Machine Learning, New York: Addison-Wesley.
16.Hagan, M. T., Demuth, H. B. and Beale, M., 1995, Neural Network Design, Boston: PWS.
17.Harrington, E., 1965, “The Desirability Function”, Industrial Quality Control, 21, pp.494-498.
18.Hsu, H. M., Tsai, S. P., Wu, M. C. and Tzuang, C. K., 1999, “A Genetic Algorithm for the Optimal Design of Microwave Filters”, International Journal of Industrial Engineering, 6, pp.282-288.
19.Hung, C. H., 1990, “A Cost-effective Multi-purpose Off-line Quality Control for Semiconductor Manufacturing”, Master’s Thesis, National Chiao Tung University, Taiwan.
20.Jung, S. and Moon, B.-R., 2002, “Toward Minimal Restriction of Genetic Encoding and Crossovers for the Two-Dimensional Euclidean TSP”, IEEE Transactions on Evolutionary Computation, 6, 6, pp.557-565.
21.Kashima, N., 1995, Passive Optical Components for Optical Fiber Transmission (Norwood: Artech House, Inc.).
22.Keung, K. W., Ip, W. H. and Lee, T. C., 2001, “The Solution of a Multi-Objective Tool Selection Model Using the GA Approach”, International Journal of Advanced Manufacturing Technology, 18, pp.771-777.
23.Kim, K.-J. and Lin, D. K. J., 2000, “Simultaneous Optimization of Mechanical Properties of Steel by Maximizing Exponential Desirability Functions”, Applied Statistics, 49, pp.311-325.
24.Khuri, A. and Conlon, M., 1981, “Simultaneous Optimization of Multiple Responses by Polynomial Regression Functions”, Technometrics, 23, pp.363-375.
25.Laviolette, M., Seaman, J. W. Jr., Barrett, J. D. and Woodall, W. H., 1995, “A Probabilistic and Statistical View of Fuzzy Methods”, Technometrics, 37, pp.249-261.
26.Lee Y. and Zak, S. H., 2002, “Designing a Genetic Neural Fuzzy Antilock-Brake- System Controller”, IEEE Transactions on Evolutionary Computation, 6, 2, pp.198-211.
27.Liao, H. Y., Liu, S. J., Chen, L. H. and Tyan, H. R., 1995, “A Bar-Code Recognition System Using Backpropagation Neural Networks,” Engineering Applications of Artificial Intelligence, 8, 1, pp. 81-90.
28.Lind, E., Goldin, J. and Hickman, J., 1960, “Fitting Yield and Cost Response Surfaces”, Chemical Engineering Progress, 56, pp.62-68.
29.Logothetis, N. and Haigh, A., 1988, “Characterizing and Optimizing Multi-Response Processes by the Taguchi Method”, Quality and Reliability Engineering International, 4, pp.159-169.
30.Lu, C. G., Morton, D., Wu, M. H. and Myler, P., 1999, “Genetic Algorithm Modeling and Solution of Inspection Path Planning on a Coordinate Measuring Machine (CMM)”, International Journal of Advanced Manufacturing Technology, 15, pp.409-416.
31.Michielssen, E., Renjithan, J.-M. S. S. and Mittra, R., 1993, ”Design of Lightweight, Broad-band Microwave Absorbers Using Genetic Algorithms”, IEEE Trans. On Microwave Theory and Techniques, 41, 6, pp.1024-1031.
32.Micusík, D., Stopjaková, V. and Benusková, L''., 2002, “Application of Feed-forward Artificial Neural Networks to the Identification of Defective Analog Integrated Circuits”, Neural Computing & Applications, 11, 1, pp.71-79.
33.Moore, D. R., Jiang, Z.-X. and Tekippe, V. J., 1996, ”Optimization of Tap Couplers Made by the FBT Process”, Proceedings of the international Conference on Fiber Optics and Photonics, Photonics-96, 2, pp.1268.
34.Mori, M., and Tseng, C. C., 1997, “Genetic Algorithm for Multi-Mode Resource Constrained Project Scheduling Problem”, European Journal of Operational Research, 100, pp.131-141.
35.NeuralWare, 2000, NeuralWorks Professional II/Plus, V5.4, Pennsylvania: NeuralWare Inc.
36.Pao, T. W., Phadke, M. S. and Sherrerd, C. S., 1985, “Computer Response Time Optimization Using Orthogonal array Experiments”, IEEE International Communications Conference, 2, pp.890-895.
37.Payne, D. B. and Stern, J. R., 1985, “Wavelength Switched, Passively Coupled, Single-Mode Optical Networks”, ECOC, 1-4 Oct. 1985.
38.Phadke, M. S., 1986, Design Optimization Case Studies, AT&T Technical Journal, 65(2), pp.51-68.
39.Phadke, M. S., 1989, Quality Engineering Using Robust Design, New Jersey: Prentice-Hall.
40.Phadke, M. S., Kackar, R. N., Speeney, D. V. and Grieco, M. J., 1983, “Off-Line Quality Control in Integrated Circuit Fabrication Using Experimental Design”, The Bell System Technical Journal, 62(5), pp.1273-1309.
41.Pignatiello, J. J. Jr., 1993, “Strategies for Robust Multiresponse Quality Engineering”, IIE Transactions, 25, pp.5-15.
42.Ponnambalam, S. G. and Ramkumar, V., 2001, “A Genetic Algorithm for the Design of a Single-Row Layout in Automated Manufacturing Systems”, International Journal of Advanced Manufacturing Technology, 18, 7, pp.512-519.
43.Qiu, M., 1997, “Prioritizing and Scheduling Road Projects by Genetic Algorithm”, Mathematics and Computers in Simulation Proceedings of the 1995 11th Biennial Conference on Modelling and Simulation, 43, pp.569-574.
44.Rooij, A. J. F. van, Jain, L.C. and Johnson, R. P., 1996, Neural Network Training Using Genetic Algorithms, World Scientific.
45.Shiau, G. H., 1990, “A Study of the Sintering Properties of Iron Ores Using the Taguchi’s Parameter Design”, Journal of the Chinese Statistical Association, 28, pp.253-275.
46.Srinivas, M. and Patnaik, L. M., 1994, “Genetic Algorithms: A Survey”, IEEE Computer, 27, 6, pp.17-26.
47.Su, C.-T. and Chiang, T.-L., 2002, “Optimal Design for a Ball Grid Array Wire Bonding Process Using a Neuro-Genetic Approach”, IEEE Transactions on Electronics Packaging Manufacturing, 25, pp.13-18.
48.Su, C.-T. and Hsu, C.-M., 1998, “Manufacturing Cell Formation Using Genetic Algorithm vs. Neural Networks,”工業工程學刊, 15, 127-139.
49.Taha, I. A. and Ghosh, J., 1999, “Symbolic Interpretation of Artificial Neural Networks”, IEEE Transactions on Knowledge and Data Engineering, 11, 3, pp.448-463.
50.Tang, K. S., Chan, C. Y., Man, K. F. and Kwong, S., 1995, ”Genetic Structure for NN Topology and Weights Optimization”, Genetic Algorithm in Engineering Systems: Innovations and Application, September 1995, pp.250-255.
51.Tong, L.-I., and Su, C.-T., 1997, “Optimizing Multi-Response Problems in the Taguchi Method by Fuzzy Multiple Attribute Decision Making”, Quality and Reliability Engineering International, 13, pp.25-34.
52.Tong, L.-I., Su, C.-T. and Wang, C.-H., 1997, “The Optimization of Multi-Response Problems in Taguchi Method”, International Journal of Quality & Reliability Management, 14, pp.367-380.
53.Valenzuela, C. L. and Wang, P. Y., 2002, “VLSI Placement and Area Optimization Using a Genetic Algorithm to Breed Normalized Postfix Expressions”, IEEE Transactions on Evolutionary Computation, 6, 4, pp.390-401.
54.Vance, R. W. C. and Love, J. D., 1995, "Back-Reflexion from Fused Biconic Couplers", Journal of Lightwave Technology, 3, 11, pp.2282-2289.
55.Vas, P., 1999, “Artificial-Intelligence-Based Electrical Machines and Drives: Application of Fuzzy, Neural, Fuzzy-Neural, and Genetic-Algorithm-Based Techniques”, Oxford university press, New York.
56.Verma, B., 1997, “Fast Training of Muitilayer Percetrons”, IEEE Transactions on Neural Networks, 8, 6, pp.1314-1320.
57.Widrow, B., Winter, R. G. and Baxter, R. A., 1987, “Learning Phenomena in Layered Neural Networks”, Proceedings of the First IEEE International Conference on Neural Netwworks, 2, pp.411-429.
58.Yang, X., 1999, “Parameter Optimization for a Microscopic Traffic Simulator Using a Genetic Algorithm and an Application Programming Interface”, M.S. Thesis, Civil Engineering, University of California.
59.Zhou, G. and Si, J., 1998, “Advanced Neural-Network Training Algorithm with Reduced Complexity Based on Jacobian Deficiency”, IEEE Transactions on Neural Networks, 9, 3, pp. 448-452.
60 .蘇木春、張孝德,1997,機器學習:類神經網路、模糊系統以及基因演算法則,全華科技圖書股份有限公司。
61. 林學煌,2000,光無源器件,人民郵電出版社。
62. 葉怡成,1997,應用類神經網路,儒林圖書公司。
QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top