We have analyzed manufacturing data from several different semiconductor
manufacturing plants, using decision tree induction software called
Q-YIELD. The software generates rules for predicting when a given product
should be rejected. The rules are intended to help the process engineers
improve the yield of the product, by helping them to discover the causes
of rejection. Experience with Q-YIELD has taught us the importance of
data engineering -- preprocessing the data to enable or facilitate
decision tree induction. This paper discusses some of the data engineering
problems we have encountered with semiconductor manufacturing data.
The paper deals with two broad classes of problems: engineering the features
in a feature vector representation and engineering the definition of the
target concept (the classes). Manufacturing process data present special
problems for feature engineering, since the data have multiple levels of
granularity (detail, resolution). Engineering the target concept is important,
due to our focus on understanding the past, as opposed to the more common
focus in machine learning on predicting the future