Data mining and fault diagnosis based on wafer acceptance test data and in-line manufacturing data

This paper focuses on techniques for automatically extracting process knowledge from a production database for fault diagnosis and optimizing device performance with a fixed target. An integrated parametric analysis scheme is developed with supplemented graphical methods to facilitate interpretation of the results. It consists of five phases: device variation partition, key node screening, linear equipment modeling, graph aided interpretation, and control policy re-evaluation. The concepts of quality control, data mining, and process knowledge are integrated in this scheme. A field data case study shows that the integrated parametric analysis scheme is able to diagnose the parametric yield problem, help engineers construct the knowledge base, predict the yield, and provide insight for yield enhancement.