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拋光製程破片關鍵影響因子辨識模型
Thesis

拋光製程破片關鍵影響因子辨識模型

謝惠如
Masters, 國立清華大學, 工業工程與工程管理學系
2009

Abstract

機台異常診斷 晶圓破片 馬氏田口 邏輯迴歸 machine diagnosis wafer brakage MTS LR
In semiconductor manufacturing, wafer breakage during polishing processes is a very serious problem. Not only the cost of wafers are very expensive, much valuable time are also lost due to problem handling and machine re-setups – not to mention delivery credibility with customers Though there are many parameters associated with the wafer polisher, it is expected only a smaller number of parameters has some relationship with wafer breakage. The goal of this research is to identify a small number of relevant parameters and/or build some index to monitoring wafer breakage potential before it actually happens so that the problem can be handled to reduce wafer breakages. This research used Mahalonobis-Taguchi System (MTS) and Logistic Regression (LR) methods to identify the key factors which are related to wafer breakage of Polisher Machine. Sensitivity, specificity, Accuracy and Area Under Curve(AUC) are used to as performance indices of the two methods. The result showed that MTS performed better than LR. The MTS indice was then chosen as a mechanism for monitoring wafer breakage potential of Polisher Machine.

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