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

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.