Abstract: A method and system of monitoring a processing system and for processing a substrate during the course of semiconductor manufacturing. As such, data is acquired from the processing system for a plurality of observations, the data including a plurality of data parameters. A principal components analysis (PCA) model is constructed from the data and includes centering coefficients. Additional data is acquired from the processing system, the additional data including an additional observation of the plurality of data parameters. The centering coefficients are adjusted to produce updated adaptive centering coefficients for each of the data parameters in the PCA model. The updated adaptive centering coefficients are applied to each of the data parameters in the PCA model. At least one statistical quantity is determined from the additional data using the PCA model. A control limit is set for the statistical quantity and compared to the statistical quantity.

Abstract: The present invention provides a method and apparatus for multivariate fault identification and classification. The method includes accessing data indicative of a plurality of physical parameters associated with a plurality of processed semiconductor wafers and providing at least one summary report including information indicative of at least one univariate representation of the accessed data and at least one multivariate representation of the accessed data.

Abstract: A method of automatically configuring an Advanced Process Control (APC) system for a semiconductor manufacturing environment in which an auto-configuration script is generated for executing an auto-configuration program. The auto-configuration script activates default values for input to the auto-configuration program. The auto-configuration script is executed to generate an enabled parameter file output from the auto-configuration program. The enabled parameter file identifies parameters for statistical process control (SPC) chart generation.