A system for online detection and classification of wafer bin map defect patterns for manufacturing intelligence

Abstract

Wafer bin maps (WBM) in circuit probe (CP) tests that present specific defect patterns provide crucial information to identifying assignable causes in the semiconductor manufacturing process. However, most semiconductor companies rely on engineers using eyeball analysis to judge defect patterns, which is time-consuming and not reliable. Furthermore, the conventional statistical process control used in CP tests only monitors the mean or standard deviation of yield rates and failure percentages without detecting defect patterns. To fill the gap, this study aims to develop a manufacturing intelligence solution that integrates spatial statistics and neural networks for the detection and classification of WBM patterns to construct a system for online monitoring and visualisation of WBM failure percentages and corresponding spatial patterns with an extended statistical process control chart. An empirical study was conducted in a leading semiconductor company in Taiwan to validate the effectiveness of the proposed system. The results show its practical viability and thus the proposed solution has been implemented in this company.

Keywords:

• wafer bin map
• data mining
• adaptive resonance theory
• manufacturing intelligence
• statistical process control
• yield enhancement
• semiconductor manufacturing

Acknowledgments

This study was supported by the National Science Council (NSC100-2628-E-007 -017 -MY3) and the Ministry of Education (100N2073E1) of Taiwan. Special thanks go to domain experts, including Smith Peng and Top Lin, for their assistance in data preparation and result validation.