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題名 IC基板製程時間之特徵選擇研究－以鑽孔作業為例
A Study of Features Selection to Process Time of IC Substrate - For Example of Drilling Operation作者 宋伯謙
Soong, Elias貢獻者 劉文卿<br>許志堅
宋伯謙
Elias Soong關鍵詞 特徵選擇
產品特徵
製程時間
資料挖礦
Features Selection
Product Characteristics
Process Time
Data-mining日期 2017 上傳時間 8-Feb-2017 16:34:37 (UTC+8) 摘要 在數據分析的領域中，尤其在大數據的領域之中，因常含有相當高維度的預測變數，故特徵選擇是一個很重要的主題。這個主題在半導體的應用上，已經獲得相當豐碩的成果，但在IC基板的應用上，成果就相對顯得貧乏。所以，此次的研究(以IC基板中鑽孔製程為例)將透過以下的試驗方法(含：GR-SNBC (Gain Ratio with Naive Bayes Classifier)、SU-SNBC (Symmetrical Uncer-tainty with Naive Bayes Classifier)與SU-CART (Symmetrical Uncer-tainty with Classification and Regression Tree Classifier))，來建立可應用於IC基板製程時間預測上的一組屬性。最後，此一研究的成果不僅在於，使用資料挖礦的方法，來找出一組具有顯著性，而且可以用來預測的IC基板製程時間的產品特徵屬性；而且，發現若為了縮短製程時間，來自產品結構本身的因子，會比來自產品在生產管理上的因子更具顯著的效果。
Feature selection is significate subject in domain of data analysis, especially in big-data with a lot of high dimension predictive variables. In semi-conductor field, this subject has already gotten a plenty of achievement, but not in IC-substrate; so in this research for example of drilling operation, through experiments, it builds a group of se-lective features for this field to predict process time, and the methods used are GR-SNBC (Gain Ratio with Naive Bayes Classifier), SU-SNBC (Symmetrical Uncertainty with Naive Bayes Classifier) and SU-CART (Symmetrical Uncertainty with Classification and Regression Tree Classifier). The contributions of this research are not only a selective product characteristics subset suggested to predict process-time in IC-substrate fab via the data-mining methods here, but also an observation that in order to shorten the process time, the factors of product construction weighs more than production management.參考文獻 [1] Backus, P.; Janakiram, M.; Mowzoon, S.; Runger, G.C.; Bhargava, A. "Factory cycle-time prediction with a data-mining ap-proach", Semiconductor Manufacturing, IEEE Transactions on, On page(s): 252 - 258 Volume: 19, Issue: 2, May 2006[2] I. Tirkel, "Cycle time prediction in wafer fabrication line by ap-plying data mining methods", Proc. 22nd IEEE/SEMIASMC, pp. 1-5, 2011[3] Y. Meidan , B. Lerner , G. Rabinowitz and M. Hassoun, "Cycle-time key factor identification and prediction in semiconductor manufacturing using machine learning and data mining", IEEE Trans. Semicond. Manuf., vol. 24, no. 2, pp. 237-248, 2011[4] Chien, C. F., Hsiao, C. W., Meng, C., Hong, K. D., Wang, S. T., 2005. Cycle time prediction and control based on production line status and manufacturing data mining, Proceedings of Inter-national Symposium on Semiconductor Manufacturing Con-ference 2005, 13-15 September, San Jose, California, USA, pp.327-330.[5] Hassoun, M. "On Improving the Predictability of Cycle Time in an NVM Fab by Correct Segmentation of the Pro-cess", Semiconductor Manufacturing, IEEE Transactions on, On page(s): 613 - 618 Volume: 26, Issue: 4, Nov. 2013[6] Dash, M., & Liu, H. (1997). Feature selection for classifica-tion. Intelligent Data Analysis, 1(1-4), 131-156.[7] Liu, H., & Motoda, H. (1998). Feature extraction, construction and selection: A data mining perspective. Norwell,MA: Kluwer Academic Publishers.[8] Liu, H., & Motoda, H. (1998). Feature selection for knowledge discovery and data mining. Norwell, MA: Kluwer Academic Publishers.[9] A. Whitney, "A direct method of nonparametric measurement selection", IEEE Transactions on Computers, vol. 20, pp.1100-1103, 1971[10] S.-H. Chung and H.-W. Huang, "Cycle time estimation for wafer fab with engineering lots", IIE Trans., vol. 34, pp. 105-118, 2002 描述 碩士
國立政治大學
資訊管理學系
103356043資料來源 http://thesis.lib.nccu.edu.tw/record/#G0103356043 資料類型 thesis dc.contributor.advisor 劉文卿<br>許志堅 zh_TW dc.contributor.author (Authors) 宋伯謙 zh_TW dc.contributor.author (Authors) Elias Soong en_US dc.creator (作者) 宋伯謙 zh_TW dc.creator (作者) Soong, Elias en_US dc.date (日期) 2017 en_US dc.date.accessioned 8-Feb-2017 16:34:37 (UTC+8) - dc.date.available 8-Feb-2017 16:34:37 (UTC+8) - dc.date.issued (上傳時間) 8-Feb-2017 16:34:37 (UTC+8) - dc.identifier (Other Identifiers) G0103356043 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/106396 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 資訊管理學系 zh_TW dc.description (描述) 103356043 zh_TW dc.description.abstract (摘要) 在數據分析的領域中，尤其在大數據的領域之中，因常含有相當高維度的預測變數，故特徵選擇是一個很重要的主題。這個主題在半導體的應用上，已經獲得相當豐碩的成果，但在IC基板的應用上，成果就相對顯得貧乏。所以，此次的研究(以IC基板中鑽孔製程為例)將透過以下的試驗方法(含：GR-SNBC (Gain Ratio with Naive Bayes Classifier)、SU-SNBC (Symmetrical Uncer-tainty with Naive Bayes Classifier)與SU-CART (Symmetrical Uncer-tainty with Classification and Regression Tree Classifier))，來建立可應用於IC基板製程時間預測上的一組屬性。最後，此一研究的成果不僅在於，使用資料挖礦的方法，來找出一組具有顯著性，而且可以用來預測的IC基板製程時間的產品特徵屬性；而且，發現若為了縮短製程時間，來自產品結構本身的因子，會比來自產品在生產管理上的因子更具顯著的效果。 zh_TW dc.description.abstract (摘要) Feature selection is significate subject in domain of data analysis, especially in big-data with a lot of high dimension predictive variables. In semi-conductor field, this subject has already gotten a plenty of achievement, but not in IC-substrate; so in this research for example of drilling operation, through experiments, it builds a group of se-lective features for this field to predict process time, and the methods used are GR-SNBC (Gain Ratio with Naive Bayes Classifier), SU-SNBC (Symmetrical Uncertainty with Naive Bayes Classifier) and SU-CART (Symmetrical Uncertainty with Classification and Regression Tree Classifier). The contributions of this research are not only a selective product characteristics subset suggested to predict process-time in IC-substrate fab via the data-mining methods here, but also an observation that in order to shorten the process time, the factors of product construction weighs more than production management. en_US dc.description.tableofcontents 中文摘要 iABSTRACT iiTable of Contents iiiTable of Figures vTable of Tables vi1 Introduction 12 Literature Review 32.1 Predictive Factors about Cycle Time 32.2 Related Work about Time Predicted 42.3 Data Mining and Knowledge Discovery in Databases 63 Methods Design 113.1 Data Preparation 113.1.1 Data Collection and Review 113.1.2 Data Integration 123.2 Data Pre-processing 143.2.1 Data Filtering 143.2.2 Data Cleaning 153.2.3 Data Transformation 173.2.4 Data Reduction 173.3 Data-Mining Method 184 Empirical Implement 254.1 Experimental Data Settlement 254.2 Experimental Approaches Implement 294.3 Experimental Result and Discussion 344.3.1 Experimental Result of Filters 344.3.2 Experimental Result of Wrappers with Filtering 364.3.3 Experimental Result of Analysis 405 Conclusion 435.1 Study Limitations 435.2 Research Contribution 435.3 Future Suggestion 44REFERENCE 45 zh_TW dc.format.extent 3662681 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0103356043 en_US dc.subject (關鍵詞) 特徵選擇 zh_TW dc.subject (關鍵詞) 產品特徵 zh_TW dc.subject (關鍵詞) 製程時間 zh_TW dc.subject (關鍵詞) 資料挖礦 zh_TW dc.subject (關鍵詞) Features Selection en_US dc.subject (關鍵詞) Product Characteristics en_US dc.subject (關鍵詞) Process Time en_US dc.subject (關鍵詞) Data-mining en_US dc.title (題名) IC基板製程時間之特徵選擇研究－以鑽孔作業為例 zh_TW dc.title (題名) A Study of Features Selection to Process Time of IC Substrate - For Example of Drilling Operation en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) [1] Backus, P.; Janakiram, M.; Mowzoon, S.; Runger, G.C.; Bhargava, A. "Factory cycle-time prediction with a data-mining ap-proach", Semiconductor Manufacturing, IEEE Transactions on, On page(s): 252 - 258 Volume: 19, Issue: 2, May 2006[2] I. Tirkel, "Cycle time prediction in wafer fabrication line by ap-plying data mining methods", Proc. 22nd IEEE/SEMIASMC, pp. 1-5, 2011[3] Y. Meidan , B. Lerner , G. Rabinowitz and M. Hassoun, "Cycle-time key factor identification and prediction in semiconductor manufacturing using machine learning and data mining", IEEE Trans. Semicond. Manuf., vol. 24, no. 2, pp. 237-248, 2011[4] Chien, C. F., Hsiao, C. W., Meng, C., Hong, K. D., Wang, S. T., 2005. Cycle time prediction and control based on production line status and manufacturing data mining, Proceedings of Inter-national Symposium on Semiconductor Manufacturing Con-ference 2005, 13-15 September, San Jose, California, USA, pp.327-330.[5] Hassoun, M. "On Improving the Predictability of Cycle Time in an NVM Fab by Correct Segmentation of the Pro-cess", Semiconductor Manufacturing, IEEE Transactions on, On page(s): 613 - 618 Volume: 26, Issue: 4, Nov. 2013[6] Dash, M., & Liu, H. (1997). Feature selection for classifica-tion. Intelligent Data Analysis, 1(1-4), 131-156.[7] Liu, H., & Motoda, H. (1998). Feature extraction, construction and selection: A data mining perspective. Norwell,MA: Kluwer Academic Publishers.[8] Liu, H., & Motoda, H. (1998). Feature selection for knowledge discovery and data mining. Norwell, MA: Kluwer Academic Publishers.[9] A. Whitney, "A direct method of nonparametric measurement selection", IEEE Transactions on Computers, vol. 20, pp.1100-1103, 1971[10] S.-H. Chung and H.-W. Huang, "Cycle time estimation for wafer fab with engineering lots", IIE Trans., vol. 34, pp. 105-118, 2002 zh_TW
