Abstract
Yield improvement is an important issue in semiconductor manufacture industry. In the color filter process, it’s critical to identify the defect result through the defect map. However, automated optical inspection (AOI) equipment cannot classify the defect types, and the defect types are determined by human now. Thus, it would cause the classified result inconsistent. In this study, we classify the defect into eight categories, and used the canny edge detection to capture the defect, and find the features. Finally, we also used the classification and regression tree (CART) to develop the automatic defect classification.