Automatic defect classification using boosting
This paper deals with automatic defect classification (ADC) in semiconductor fabrication. The defects such as particle and scratch are automatically classified using a boosting approach. The boosting scheme is based on the Kullback-Leibler distance and linear projection along feature vectors. The paper generates the linear features which discriminate the defects maximally. The features are the linear combinations of Haar-like patterns in the frequency domain. By learning in a boosting manner, the particle and scratch are recognized out of the other defects. And, we propose another feature in the spatial domain which is based on the orientation histogram in the local region. The spatial feature is combined with frequency domain features in a boosting manner. According to the experiments with various defect samples, the accuracy of defect classification is larger than 92% on the average, and scratch is especially recognized with 98% purity. More improvement is expected by the new spatial features such as defect colors, shapes, textures, and so on.