運用模式樹建構半導體測試時間之診斷模式

康鈞誌

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運用模式樹建構半導體測試時間之診斷模式

Using model tree to build up diagnostic model of semiconductor probing time

康鈞誌 , Masters　　Advisor：翁慈宗　　

繁體中文

數值預測；模式樹；線性迴歸式； linear regression model ； numeric prediction ； model tree

Abstract │ Reference (20) │ Altmetrics

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Living in the age that information flows so fast. How to convert data into useful information becomes an important issue. Model trees have a tree structure with leaves containing linear regression models. They could reveal the relationship between attributes and the information embedded in data. The data gather in wafer testing factory contain attributes about operators, machines, material, and processes. An attribute type can be either numeric or nominal. Model tree is originally designed for processing numeric attributes. M5’ could deal with both attribute types, hence this study adopts this tool for building model trees. Before building a model tree, the preprocess of data includes data integration, data clearance, data transformation, and data normalization. Then data will be divided into normal and abnormal groups based on their wafer testing times. The model trees built for the two data groups are compared to distinguish their differences in internal nodes and leaf nodes. The attributes that are critical for increasing the test time of a wafer are extracted, and this information could let related staffs know the appropriate steps for dealing with an abnormal wafer.

Reference ( 20 ) 〈TOP〉

湯穎奇 (2005)，應用K-means 分群演算法於選取模式樹節點屬性之研究，碩士論文，國立成功大學工業與資訊管理研究所。
連結：
陳建智 (2007)，應用模式樹建構印刷電路板裝配之表面黏著製程品質管制模型之研究，碩士論文，國立成功大學工業與資訊管理研究所。
連結：
郭信宏 (2008)，應用資料探勘技術於面板檢測實證研究，碩士論文，國立中央大學工業管理研究所。
連結：
Backus, P., Janakiram, M., Mowzoon, S., Runger, G.C., and Bhargava, A. (2006). Factory cycle-time prediction with a data-mining approach, IEEE Transactions on Semiconductor Manufacturing, 19(2), 252-258.
連結：
Chien, C.F., Wang, W.C., and Cheng, J.C. (2007). Data mining for yield enhancement in semiconductor manufacturing and an empirical study, Export Systems with Applications, 33, 192-198.
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