Combining Feature Extraction-Based and Full Trace Analysis Capabilities in Fault Detection: Methods and Comparative Analysis

As semiconductor manufacturing processes become more complex and sophisticated, manufacturers demand high quality, efficient process monitoring and fault detection (FD). Segmentation feature extraction (SFE) has been shown to be successful in FD when the anomaly patterns are well described by feature extraction, but not in cases of complex anomaly patterns. This paper proposes a novel approach that combines feature extraction-based and full trace analysis (FTA) capabilities in FD. First, a novel multivariate trace segmentation is applied to segment trace data based on defined patterns across key sensors, and then the pattern features and parameters are extracted within each segment. Second, full trace analysis generates adaptive guardbands on key sensors. These guardbands detect “anomaly” throughout the whole trace with a certain confidence interval. Third, a multivariate fault detection model is developed that combines SFE and FTA capabilities. Furthermore, multivariate data generation is developed to exhaustively validate combination and isolation of the proposed capabilities. Experimental results indicate that FTA can better capture the anomaly features with complex/atypical patterns, and the combined capability of SFE + FTA exhibits better performance than each method applied individually.