Abstract: A method for estimating a state associated with a process includes receiving a state observation associated with the process. The state observation has an associated process time. A weighting factor to discount the state observation is generated based on the process time. A state estimate is generated based on the discounted state observation. A system includes a process tool, a metrology tool, and a process controller. The process tool is operable to perform a process in accordance with an operating recipe. The metrology tool is operable to generate a state observation associated with the process. The process controller is operable to receive the state observation, the state observation having an associated process time, generate a weighting factor to discount the state observation based on the process time, generate a state estimate based on the discounted state observation, and determine at least one parameter of the operating recipe based on the state estimate.

Abstract: A method for estimating a state associated with a process includes receiving a state observation associated with the process. The state observation has an associated process time. A weighting factor to discount the state observation is generated based on the process time. A state estimate is generated based on the discounted state observation. A system includes a process tool, a metrology tool, and a process controller. The process tool is operable to perform a process in accordance with an operating recipe. The metrology tool is operable to generate a state observation associated with the process. The process controller is operable to receive the state observation, the state observation having an associated process time, generate a weighting factor to discount the state observation based on the process time, generate a state estimate based on the discounted state observation, and determine at least one parameter of the operating recipe based on the state estimate.

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: 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.