Application of PCA for efficient multivariate FDC of semiconductor manufacturing equipment

With the evolutions in sensing technologies and the increasing use of advanced process control techniques, terabytes of data are recorded today from manufacturing equipment during the process of semiconductor devices. These large amounts of data are then operated by FDC systems to assess the overall condition of the equipment. In this paper, we consider the Exponential Hybrid-wise Multiway Principal Components Analysis (E-HMPCA), a PCA-derived model that include an Exponentially Weighted Moving Average component, for the condition monitoring of a Chemical Vapor Deposition tool in STMicroelectronics Rousset 8” fab. In order to work directly on temporal signal from equipment sensors, the application of Dynamic Time Warping for data synchronization is also presented. A real-occurred failure case is used to highlight the benefits of this approach on detection efficiency improvement and monitoring complexity reduction.