運用資料探勘技術建構半導體封裝業之品質改善系統__臺灣人文及社會科學引文索引資料庫

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題名：	運用資料探勘技術建構半導體封裝業之品質改善系統
作者：	吳瑞琦
作者(外文)：	Ruey-Chyi Wu
校院名稱：	國立交通大學
系所名稱：	資訊管理研究所
指導教授：	陳瑞順
學位類別：	博士
出版日期：	2006
主題關鍵詞：	資料探勘；資料倉儲；群集；決策樹；類神經網路；羅吉斯回歸；貝式；關聯法則；線上分析處理；捲帶式封裝；多準則決策；Data mining；Data warehouse；Clustering；Decision tree；Neural network；Logistic regression；Bayesian；Association rule；OLAP；TCP；Multiple criteria decision making
原始連結：	連回原系統網址
相關次數：	被引用次數:期刊(0) 博士論文(1) 專書(0) 專書論文(0) 排除自我引用:0 共同引用:0 點閱:62

在半導體封裝產業中，製程的良率一直是同業競爭中的關鍵因素，有鑑於此，本研究乃針對提高製程良率而建置一套品質改善系統。個案將以液晶顯示器驅動積體電路之捲帶式封裝製程為研究主體，透過改善關鍵製程來提高良率，以減少因為未將製程參數最佳化所產生的瑕疵。
首先，我們將蒐集來的失效產品資料經由群集演算法探勘獲得瑕疵種類分群的數量，然後決定出各群的定義，並導入資料倉儲中。接著再採用適合分類之演算法，如決策樹、類神經網路、羅吉斯迴歸、貝氏分類與關聯規則等，從中評估與比較以挑選出最佳的演算法來進行關鍵變數之分析。
由本研究的結果，顯示出不良產品的關鍵因素並探討出問題解決對策，以提供品質管制工程人員做為製程參數最佳化之參考。經導入本品質改善系統後，大大地提昇了整體產品良率、減少了重工/停機次數與縮短了產品生產週期，因此對半導體封裝業而言，本研究是提昇競爭力以因應瞬息萬變市場的有利工具。

以文找文

Yield enhancements are always the key factor among competitors in the semiconductor packaging industry. In view of this, this paper attempts to construct a quality improvement system to increase process yield. Case study focuses mainly on the tape carrier package process of liquid crystal display driver IC by improving key process for the increase of yield rate. The parameter settings should be taken into account to avoid that the defects happen due to process parameters not being optimized.
First, we collect large amounts of data of defective products and use clustering algorithm to obtain the amount of defective categories. Next, the definitions of these clusters are determined and loaded into the data warehouse. Then, the suitable classification algorithms, decision tree, neural network, logistic regression, Bayesian, and association rule, are used for evaluation and comparison to select a better algorithm for the analysis of key variables.
From the result of this research, major causes of defective products are investigated and countermeasures of the problems will provide know-how for the optimization of process parameters to quality control engineers. After the proposed quality improvement system is applied, the benefits are obtained to improve product yield reduction of rework/holding lot frequency and product cycle time. Therefore, to semiconductor packaging industry, this study provides a most beneficial tool to improve competitiveness for responding to the ever changing marketplace.

以文找文

Reference
[1]Agrawal, R., Imilienski, T. and Swami, A., “Mining Association Rules between Sets of Items in Large Databases”, In Proceedings of ACM SIGMOD international Conference on Management of Data, pp. 207-216, 1993.
[2]Berry, Michael J. A., Gordon S. Linoff, “Data mining techniques: for marketing, sales, and customer support”, John Wiley & Sons, New York, pp. 297-305, 1997.
[3]Brachman, R. J., T. Khabaza, W. Kloesgen, G.P. Shapiro, E. Simoudis, “Mining business databases”, Communications of the ACM, 39(11), pp. 42-48, 1996.
[4]Cabena, P., P. O. Hadjinian, R. Stadler, DR. J. Verhees, and A. Zanasi, ”Discovering Data Mining from Concept to Implementation”, Prentice Hall, pp. 12, 1997.
[5]Chaudhuri, Surajit, Umeshwar Dayal, “An overview of data warehousing and OLAP technology”, SIGMOD, 26, pp. 65-74, 1997.
[6]Chen, S. W. et al., “Electronic packaging technology and material”, Gau Lih Books Co., Ltd., pp. 149-161, 2004.
[7]Chen, Zhengxin, “Data mining and uncertain reasoning: an integrated approach”, John Wiley & Sons, New York, pp. 81-84, 2001.
[8]Fan, Cang-Ren, “A Web-Based Integrated Data Mining System for Computer Integrated Manufacturing- A Case Study On Semiconductor Packaging Industry”, Institute of Information Management, National Chiao Tung University, pp. 61-63, 2003.
[9]Frawley, W. J., G. Paitetsky-Shapiro, C. J. Matheus, “Knowledge discovery in database: an overview. Knowledge Discovery in Database”, AAAI/MIT Press, pp. 1-30, 1991.
[10]Han, Jiawei, Micheline Kamber, “Data mining: concepts and techniques”, Morgan Kaufmann Publishers, pp. 41-78, pp. 285-328, 2001.
[11]Hsieh, C. B., “Data Mining and Business intelligence: SQL Server 2005”, Ting Mao Publish Company, pp. 129-149, pp. 153-170, pp. 278-291, 2005.
[12]Hsu, G. H., “An Advanced Packaging Technology: Wafer Packaging Technology”, Materials Magazine, 151, pp. 86-91, 1991.
[13]Huang, Cheng-Lung, ”Semiconductor Production Rate Prediction with Data Mining Approach”, Information Management, Hua Fan University, pp. 36-44 2004.
[14] Inmon, W. H., “Building the Data Warehouse”, John Wiley & Sons, New York, pp. 71-135, 1996.
[15]Kleissner, C., “Data mining for the enterprise”, IEEE Proceedings of the 31st Annual Hawaii International Conference on System Sciences, Vol.7, pp. 295-304, 1998.
[16]Lee, Perry, “Constructing A Semiconductor Manufacturing Data Mining Framework, Developing A Decision Tree Algorithm for Classification, and Conducting Empirical Studies”, Industrial Engineering and Engineering Management, National Tsing Hua University, pp. 66-78, 2002.
[17]Quinlan, J. Ross, “C4.5: Programs for machine learning”, Morgan Kaufmann Publishers, pp. 81-106, 1993.
[18]Rau H., J. W. Tzeng, L. S. Huang, “Defect classification and recognition for semiconductor manufacturing packaging”, pp. 849-855, 1998.
[19]Shen, C. Y., “Data warehouse and analysis services: SQL Server 2000 OLAP solutions”, Kings Information Co. Ltd., pp. 2.7-2.8, 2001.
[20]Tsai, Tsan-Lian, “Research in Taiwan LCD driver IC finishing process optimum work distribution model”, National Chiao Tung University Masters in High Executive Management, pp. 11-21, 2001.
[21]Tseng, S. S. et al., “Data mining”, Flag Publishing Co., Ltd, pp. 5.24-35, 2005.
[22]Tseng, Wei-Lin, “The Development of Internal Process Quality Handling Model for Printed Circuit Board Industries Using Data Mining Techniques”, Industrial Engineering and Management, Yuan Ze University, pp. 53-71, 2004.
[23]Wang, Chin-Tsan, Qiu Zhi-Wei, Xu Chuan-Yuan and Chen Ming- Zhen, “A Study of Quality Theorems on Process Capability of Mechanic Manufacturing Industry - An Example of MH Company”, The 6th Reliability and Maintainability Symposium Proceedings, pp. 1-15, 2003.
[24]Weiss, S. M. and C. A. Kulikowski, “Computer Systems That Learn—Classification and Prediction Methods from Statistics, Neural Nets, Machine Learning, and Expert System”, Morgan Kaufmann, pp. 93-110, 1991.
[25]Yang et al., “Analysis and reliability assessment in inner lead welding machine characteristics for tape carrier package IC”, Electronics and Materials Magazine, pp. 132-142, 2001.
[26]Yin, H. G., “SQL Server 2005 Data Mining Bible”, XBOOK Marketing Co., pp. 4.3-4.21, pp. 5.5-5.14, pp. 6.3-6.9, pp. 7.7-7.21, pp. 10.7-10.17, pp. 12.3-12.8, pp. 15.2-15.20, 2006.
[27]http://ieknet.itri.org.tw/_~~mvc2~mvc1_glwpmgn/_~~publish~pdf_upload~xyz/2004/CAEE30904C2A91C048256EC50050F189/index.htm.
[28]http://www.kdnuggets.com/polls/2006/data_mining_methods.htm.
[29]http://www.microsoft.com/taiwan/sql/prodinfo/overview/whats-new-in-sqlserver2005.mspx.
[30]http://www.microsoft.com/taiwan/technet/prodtechnol/sql/2000/maintain/dmperf.aspx.
[31]http://cdnet.stpi.org.tw/techroom/analysis/pat_A066.htm.
[32]http://msdn2.microsoft.com/en-us/library/ms174879.aspx
[33]http://msdn2.microsoft.com/en-us/library/ms175312.aspx
[34]http://msdn2.microsoft.com/en-us/library/ms174941.aspx
[35]http://msdn2.microsoft.com/en-us/library/ms174828.aspx
[36]http://msdn2.microsoft.com/en-us/library/ms174806.aspx
[37]http://msdn2.microsoft.com/en-us/library/ms174916.aspx
[38]http://www.ceci.org.tw/book/53/ch53_1.htm

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