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

Authors:
Li-Chang Chao;Lee-Ing Tong
Affiliations:
Department of Industrial Engineering and Management, National Chiao Tung University, 1001 Dah-Hsei Road, Hsin-Chu 300, Taiwan, ROC and Department of Industrial Engineering and Management, Diwan Un ...;Department of Industrial Engineering and Management, National Chiao Tung University, 1001 Dah-Hsei Road, Hsin-Chu 300, Taiwan, ROC
Venue:
Expert Systems with Applications: An International Journal
Year:
2009

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Abstract

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.