Reconfigured piecewise linear regression tree for multistage manufacturing process control

Abstract

In a multistage manufacturing process, extensive amounts of observational data are obtained by the measurement of product quality features, process variables, and material properties. These data have temporal and spatial relationships and may have a non-linear data structure. It is a challenging task to model the variation and its propagation using these data and then use the model for feedforward control purposes. This article proposes a methodology for feedforward control that is based on a piecewise linear model. An engineering-driven reconfiguration method for piecewise linear regression trees is proposed. The model complexity is further reduced by merging the leaf nodes with the constraint of the control accuracy requirement. A case study on a multistage wafer manufacturing process is conducted to illustrate the procedure and effectiveness of the proposed method.