Semi-supervised multi-label learning for classification of wafer bin maps with mixed-type defect patterns

Abstract

After wafer fabrication, individual chips on a wafer are checked whether they are defective or not through multiple electrical tests. The test results can be represented by binary values for all individual chips, which form a spatial map called a wafer bin map (WBM). Different defect patterns in WBMs are related to different causes of process faults, and thus it is important to classify WBMs according to their defect patterns to identify root causes of process faults and fix the problems. Recently, as the wafer size has increased and the semiconductor manufacturing process has become more complicated, probability of having mixed type defect patterns on wafer bin maps has increased. Previous studies for the classification of mixed-type defect patterns mainly used labeled WBM data only, although a much larger amount of unlabeled data are often available in practice. To utilize both labeled and unlabeled data to achieve better classification performance, this study proposes the use of semi-supervised deep convolutional generative model. In particular, we formulate the problem of classifying mixed-type defect patterns as a problem of multi-label classification, and adopt multiple latent class variables, each for a distinct single pattern. As an inherent advantage of a generative model, we can also use the proposed model to generate new WBM data.