| Summary: | 碩士 === 國立交通大學 === 電機與控制工程系所 === 94 === This paper aims to look out for the flexible working region of predictor variable under the condition of meeting the desirability of response variable. The region will be completed as wide as possible. Besides, find out the rank of important predictor variable which will affects working region. Compare to many traditional methods, which merely find out the optimal point, this paper takes one step ahead to look out for the working region of predictor variable and uses hyper rectangular cuboid to state this region. Hence, it will be more flexible when we control predictor variable.
This experiment takes wafer manufacturing process data as an example, hoping the yield will be greater than or equal to 95%. Under this situation, we want to find out the flexible working region of predictor variable. Fist, we take data to feature selection, rid of redundancy and outlier in data preprocession. Next, we use the preprocessed data to build up six memory- based models to execute regression. Afterward, we use classification trees to classify and decide the working region. Finally, utilize validation data to confirm the decided working region.
The same method can be applied not only to wafer manufacturing process in this paper, but to the fields of finance and medical science, etc….
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