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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 |
| Authors: | 宋伯謙
Soong, Elias |
| Contributors: | 劉文卿
許志堅
宋伯謙
Elias Soong |
| Keywords: | 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 |
| Issue Date: | 2017-02-08 16:34:37 (UTC+8) |
| Abstract: | 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. |
| Reference: | [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 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0103356043 |
| Data Type: | thesis |
| Appears in Collections: | [資訊管理學系] 學位論文
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