Abstract: A process module tuning method characterizes a process module by gathering data using a process condition measuring device to measure process outputs while inputs are excited. The data is used to identify a dynamic process model. The dynamic process model is then be used to determine process input settings that will produce desired outputs. For multi-zone process modules, the interactions between zones may be modeled.

Abstract: A system for determining a leak rate of a device during an adiabatic cooling phase is provided. The system includes a leak rate training system receiving leak rate calibration data, such as pressure data or mass flow data for a device having a known leakage rate. The leak rate training system generates an adiabatic cooling model from the leak rate calibration data, such as by solving a finite difference equation for one or more unknown process variables that are dependent on the adiabatic cooling parameters of the device. The system further includes a leak rate detection system receiving the adiabatic cooling model and leak rate data and determining a leak rate component of the leak rate data using the adiabatic cooling model, such as by using the variable values determined through solving the finite difference model to interpolate between the leak rate calibration data that was gathered using known leakage rates.

Abstract: A system for determining a leak rate of a device during an adiabatic cooling phase is provided. The system includes a leak rate training system receiving leak rate calibration data, such as pressure data or mass flow data for a device having a known leakage rate. The leak rate training system generates an adiabatic cooling model from the leak rate calibration data, such as by solving a finite difference equation for one or more unknown process variables that are dependent on the adiabatic cooling parameters of the device. The system further includes a leak rate detection system receiving the adiabatic cooling model and leak rate data and determining a leak rate component of the leak rate data using the adiabatic cooling model, such as by using the variable values determined through solving the finite difference model to interpolate between the leak rate calibration data that was gathered using known leakage rates.

Abstract: A multi-input multi-output (MIMO) controller is configured as a generic, non-interacting, multivariable controller with adaptive capabilities. The controller transforms the coupled multivariable control problem to multiple decoupled single-input single-output loops. The decoupling occurs in the input/output map of the system being controlled. The transformed system can then be controlled using any control synthesis technique to meet the system performance objectives, such as Proportional plus Integral plus Derivative (PID) control. An empirical dynamic model is continually identified from on-line data. From this model, static decoupling transformations based on a singular value decomposition are determined and updated and the controller gains are calculated.