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| Title | 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 |
| Creator | 宋伯謙 Soong, Elias |
| Contributor | 劉文卿<br>許志堅 宋伯謙 Elias Soong |
| Key Words | 特徵選擇 產品特徵 製程時間 資料挖礦 Features Selection Product Characteristics Process Time Data-mining |
| Date | 2017 |
| Date Issued | 8-Feb-2017 16:34:37 (UTC+8) |
| Summary | 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 |
| Description | 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 |
| Type | 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 i ABSTRACT ii Table of Contents iii Table of Figures v Table of Tables vi 1 Introduction 1 2 Literature Review 3 2.1 Predictive Factors about Cycle Time 3 2.2 Related Work about Time Predicted 4 2.3 Data Mining and Knowledge Discovery in Databases 6 3 Methods Design 11 3.1 Data Preparation 11 3.1.1 Data Collection and Review 11 3.1.2 Data Integration 12 3.2 Data Pre-processing 14 3.2.1 Data Filtering 14 3.2.2 Data Cleaning 15 3.2.3 Data Transformation 17 3.2.4 Data Reduction 17 3.3 Data-Mining Method 18 4 Empirical Implement 25 4.1 Experimental Data Settlement 25 4.2 Experimental Approaches Implement 29 4.3 Experimental Result and Discussion 34 4.3.1 Experimental Result of Filters 34 4.3.2 Experimental Result of Wrappers with Filtering 36 4.3.3 Experimental Result of Analysis 40 5 Conclusion 43 5.1 Study Limitations 43 5.2 Research Contribution 43 5.3 Future Suggestion 44 REFERENCE 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 |