Wafer defect pattern recognition by multi-class support vector machines by using a novel defect cluster index

Wafer yield is an important index of efficiency in integrated circuit (IC) production. The number and cluster intensity of wafer defects are two key determinants of wafer yield. As wafer sizes increase, the defect cluster phenomenon becomes more apparent. Cluster indices currently used to describe this phenomenon have major limitations. Causes of process variation can sometimes be identified by analyzing wafer defect patterns. However, human recognition of defect patterns can be time-consuming and inaccurate. This study presents a novel recognition system using multi-class support vector machines with a new defect cluster index to efficiently and accurately recognize wafer defect patterns. A simulated case demonstrates the effectiveness of the proposed model. 2009 Elsevier Ltd. All rights reserved.