Weighted principal component analysis and its applications to improve FDC performance

This paper discusses several practical issues encountered when applying principal component analysis (PCA) to fault detection and classification (FDC). Those issues presented a challenge to the success of PCA-model based process monitoring. Weighted PCA is proposed to address the issues. Two forms of weighted PCA are discussed: sample-wise and variable-wise. Sample-wise weighted PCA is used to address issues with model updating. By adapting models with process changes, the long-term validity of a PCA model can be maintained. We also propose to deal with drift and shift features separately. Variable-wise weighted PCA is used to incorporate process and sensor knowledge. PCA models built this way require less maintenance and result in better FDC performance. We performed comparison studies on plasma etcher FDC. We have shown that weighted PCA can adapt to process drift, reduce the occurrence of false alarms and make the models easy to maintain.