Constructing control process for wafer defects using data mining technique

Abstract

The wafer defects influence the yield of a wafer. The integrated circuits (IC) manufacturers usually use a Poisson distribution based c-chart to monitor the lot-to-lot wafer defects. As the wafer size increases, defects on wafer tend to cluster. When the c-chart is used, the clustered defects frequently cause erroneous results. The main objective of this study is to develop a hierarchical adaptive control process to monitor the clustered defects effectively and detect the wafer-to-wafer variation and lot-to-lot variation simultaneously using data mining technique.