  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.
