Improvement of Multi-Lines Bridge Defect Classification by Hierarchical Architecture in Artificial Intelligence Automatic Defect Classification

Defect classifications are the very important steps as the in-line defect inspection of the semiconductor manufacturing procedure. The conventional defect classifications are usually through visual judgement by engineer or technical assistant. However, it's time-consuming and laborious. In our recent study, the artificial intelligence automatic defect classification (AI-ADC) performed promisingly good accuracy and purity (A/P) of the auto defect classification by deep learning method. Nevertheless, some kinds of tiny defects are not only suffered lower A/P issues, but also suffered bad A/P stability of real defect classification. In this work, we propose the novel method, called "Hierarchical structure AI-ADC", which introduced a second binning classifier and it's based on hierarchical clustering to achieve more precise defect classification. As a result, the proposed method shown obvious improvements to the binning purity of multi-lines bridge defect from 56% to 88% as well as the stability variation has been reduced from 55% to 22%, besides it also can be applied to classify the similar defect types efficiently. Indeed this approach achieves excellent defect classification and highly stable performance.