Ibex Process Technology, Inc.

Jill P Card

Wai T Chan

Edward Rietman

An Cao

Edward A Reitman

The health of a tool is predicted based on temporally ordered input data representing parameters indicative of tool health. A sliding time window is used to partition input data into temporally displaced data sets. Non-linear regression models determine, based on the data sets, a set of predictive values relating to tool health at a future time. A tool-health metric is then determined based on one or more of the predictive values.

Intelligent modelling of process and tool health

Complex process control and maintenance are performed utilizing a nonlinear regression analysis to determine optimal maintenance activities and process adjustments based on an urgency metric.

Intelligent control for process optimization and parts maintenance

Failure prediction for complex processes is performed utilizing one or more nonlinear regression models to relate operational variable values measured at two or more times to predicted process metric values and maintenance variable values.

Advance failure prediction

Systems and methods of complex process control utilize driving factor identification based on nonlinear regression models and process step optimization. In one embodiment, the invention provides a method for generating a system model for a complex process comprised of nonlinear regression models for two or more select process steps of the process where process steps are selected for inclusion in the system model based on a sensitivity analysis of an initial nonlinear regression model of the process to evaluate driving factors of the process.

Large scale process control by driving factor identification

The present invention provides a method and system for complex process optimization utilizing metrics, operational variables, or both, of one or more process steps and optimization of one or more of these process step parameters with respect to a cost function for the parameter. In one embodiment, the invention provides a scalable, hierarchical optimization method utilizing optimizations at one process level as inputs to an optimization of a higher or lower process level.

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West Newbury, MA, United States of America

Jill P Card