Semi-supervised anomaly detection in mask writer servo logs : An investigation of semi-supervised deep learning approaches for anomaly detection in servo logs of photomask writers

Abstract: Semi-supervised anomaly detection is the setting, where in addition to a set of nominal samples, predominantly normal, a small set of labeled anomalies is available at training. In contrast to supervised defect classification, these methods do not learn the anomaly class directly and should have better generalization capability as new kinds of anomalies are introduced at test time. This is applied in an industrial defect detection context in the logs of photomask writers. Four methods are compared: two semi-supervised one-class anomaly detection methods: Deep Semi-Supervised Anomaly Detection (DeepSAD), hypersphere classifier (HSC) and two baselines, a reconstructive GAN method based on the Dual Autoencoder GAN (DAGAN) and a non-learned distance method based on the Kullback-Leibler divergence. Results show that semi-supervision increases performance, as measured by ROC AUC and PRO AUC, of DeepSAD and HSC, but at the tested supervision levels, do not surpass the performance of DAGAN. Furthermore, it is found that autoencoder pretraining increases performance of HSC similarly to as it does for DeepSAD, even though only the latter is recommended in literature. Lastly, soft labels are utilized for HSC, but results show that this has no or negative effect on the performance.