Regression methods for prediction of PECVD Silicon Nitride layer thickness

Abstract

Different approaches for the prediction of average Silicon Nitride cap layer thickness for the Plasma Enhanced Chemical Vapor Deposition (PECVD) dual-layer metal passivation stack process are compared, based on metrology and production equipment Fault Detection and Classification (FDC) data. Various sets of FDC parameters are processed by different prediction algorithms. In particular, the use of high-dimensional multivariate input data in comparison to small parameter sets is assessed. As prediction methods, Simple Linear Regression, Multiple Linear Regression, Partial Least Square Regression, and Ridge Linear Regression utilizing the Partial Least Square Estimate algorithm are compared. Regression parameter optimization and model selection is performed and evaluated via cross validation and grid search, using the Root Mean Squared Error. Process expert knowledge used for a priori selection of FDC parameters further enhances the performance. Our results indicate that Virtual Metrology can benefit from the usage of regression methods exploiting collinearity combined with comprehensive process expert knowledge.