New anomaly detection in semiconductor manufacturing process using oversampling method

Quality in the semiconductor manufacturing process, consisting of various production systems, leads to economic factors, which necessitates sophisticated abnormal detection. However, since the semiconductor manufacturing process has many sensors, there is a problem with the curse of dimensionality. It also has a high imbalance ratio, which creates a classification model that is skewed to multiple class, thus reducing the class classification performance of a minority class, which makes it difficult to detect anomalies. Therefore, this paper proposes AEWGAN (Autoencoder Wasserstein General Advertising Networks), a method for efficient anomaly detection in semiconductor manufacturing processes with high-dimensional imbalanced data. First, learn autoencoder with normal data. Abnormal data is oversampled using WGAN (Wasserstein General Additional Networks). Then, efficient anomaly detection within the potential is carried out through the previously learned autoencoder. Experiments on wafer data were applied to verify performance, and of the various methods, AEWGAN was found to have excellent performance in abnormal detection.