Monitoring Wafer Defects and Clustering in IC Fabrication using Multivariate Exponentially Weighted Moving Average Control Chart

• Main Authors: Yang Ou, 歐陽
• Other Authors: Lee-Ing Tong
• Format: Others
• Language: zh-TW
• Published: 2005
• Online Access: http://ndltd.ncl.edu.tw/handle/86901082633047185301

碩士 === 國立交通大學 === 工業工程與管理系所 === 93 === The c-chart is widely used in the integrated circuits (IC) manufacturers to monitor the wafer defects. However, the clustering of defects on a wafer due to the complicated manufacturing process becomes more evident with increased wafer size. The defect clustering phenomenon causes c-chart invalid, and the false alarms often appeared. The modified c-chart such as Neyman Type-A distribution and Hotelling’s T2 multivariate control chart still have some the disadvantages for defect clustering phenomenon and sensitivity. In addition, if there are too many defect counts or the serious clustering phenomenon on wafer surface that expressed the process possibly had problems; therefore, the control chart should monitors defect counts and defect clustering phenomenon simultaneously. The objective of this study is to develop a Multivariate Exponentially Weighted Moving Average (MEWMA) process control method for monitoring wafer defect with clustering phenomenon simultaneously. This study employed MEWMA control chart using the defect counts and cluster index as two quality characteristics. The proposed method employs the technique of decomposition of T2 to determine which quality characteristic (or the interaction of both quality characteristics) causes the process to out of control. The simulated data and a real case study are also presented, the results indicate that the proposed method is more effective than that of previous studies.