Armin Shmilovici Leib

This paper delineates a comprehensive and successful application of decision tree induction to 1054 records of production lots taken from a lithographic process with 45 processing steps. Complex interaction effects among manufacturing equipment that lead to increased product variability have been detected. The extracted information has been confirmed by the process engineers, and used to improve the lithographic process. The paper suggests that decision tree induction may be particularly useful when data is multidimensional, and the various process parameters and machinery exhibit highly complex interactions. Another implication is that on-line monitoring of the manufacturing process (e.g., closed-loop critical dimensions control) using data mining may be highly effective.