Anomaly detection for sensor data of semiconductor manufacturing equipment using a GAN

Abstract

In semiconductor manufacturing, it is required to detect anomalies which cause expensive defects. In recent years, Generative Adversarial Networks (GANs) have played a big role in anomaly detection. This study aims to detect anomalies by analyzing sensor data using a GAN when multivariate time series of sensor data are given. Our GAN could detect anomalies which cannot be detected visually. Experimental results indicated that an attention mechanism could tell us important sensors in detecting anomalies.