To prevent low yields in the semiconductor industry is crucial to the success of that industry.
However, to prevent low yields is difficult because of too many factors to affect yield variation and
their complex relation in the semiconductor manufacturing process. This study presents a new efficient
detection methodology for detecting abnormal yields including high and low yields, which can forecast
the yield level of a production unit (namely a lot) based on yield-related feature variables' behaviors.
In the methodology, we use C5.0 to identify the yield-related feature variables that are the combination
of correlated process variables associated with yield, use SOM (Self-Organizing Map) neural
networks to extract and classify significant patterns of past abnormal yield lots and finally use C5.0 to
generate classification rules for detecting abnormal yield lot. We illustrate the effectiveness of our
methodology using a semiconductor manufacturing company’s field data.