Anomaly detection of semiconductor processing equipment using equipment behaviour

Abstract

As semiconductor design rules evolve, the required level of reliability for semiconductor processing equipment is increasing. It is impossible to detect anomalies simply by checking a single factor, the oxygen concentration, which is the most important indicator of the equipment performance. We extracted 16 features from the behaviour of oxygen concentration and pressure in the load area, and built univariate and multivariate models by using logistic regression with these features. The proposed method was able to detect anomalous equipment that could not be detected by monitoring only the oxygen concentration, and greatly shortened the processing lead time including adjustment.