A stacking ensemble classifier with handcrafted and convolutional features for wafer map pattern classification

Hyungu Kang, Seokho Kang

Abstract: Highlights • We propose a hybrid method for improving wafer map pattern classification. • Manual feature extraction and CNN have different strengths for different defect classes. • This method leverages the respective advantages of manual feature extraction and CNN. • The effectiveness is demonstrated using a real-world dataset. Abstract Recently, machine learning has been effectively applied in the automation of wafer map pattern classification in semiconductor manufacturing. One conventional approach is to extract handcrafted features from a wafer map and build an off-the-shelf classifier on top of the features. Another approach is to use a convolutional neural network that operates directly on a wafer map. These two approaches have different strengths for different classes of wafer map defect patterns. In this study, we present a hybrid method that leverages the advantages of both approaches to improve the classification accuracy. First, we build two base classifiers using each of the approaches. Then, we build a stacking ensemble that combines the outputs of these base classifiers for the final prediction. The stacking ensemble classifies a wafer map by assigning a larger weight to the output of the superior base classifier with respect to each defect class. We demonstrate the effectiveness of the proposed method using real-world data from a semiconductor manufacturer.