摘要
Yield improvement is an important issue in semiconductor-manufacturing supply chains, including color filter and micro-lens manufacturing. In the color filter and micro-lens processes, it is critical to quickly identify the defect pattern through the defect pictures and then take corrective actions to avoid greater yield loss. Until now, defect pattern recognition heavily relies on domain experts' knowledge, which easily causes inconsistent classification results from person to person and unsatisfactory performance. In this study, a framework is proposed to achieve automatic defect detection and classification in color filter and micro-lens manufacturing to enhance the decision quality of pattern recognition. In particular, the proposed framework integrates Canny edge detection and classification and regression tree methodology. To validate the viability of the proposed framework in real settings, an empirical study was conducted in collaboration with a leading complementary metal oxide semiconductor image sensor foundry in Taiwan. The results not only showed the effectiveness of the proposed framework but also demonstrated the practical values. © 2013 Chinese Institute of Industrial Engineers.