Compensation-oriented quality control in multistage manufacturing processes

Abstract

Significant research has been initiated recently to devise control strategies that could predict and compensate manufacturing errors using so called explicit Stream-of-Variation(SoV) models that relate process parameters in a Multistage Manufacturing Process (MMP) with product quality. This doctoral dissertation addresses several important scientific and engineering problems that will significantly advance the model-based, active control of quality in MMPs. First, we will formally introduce and study the new concept of compensability in MMPs, analogous to the concept of controllability in the traditional control theory. The compensability in an MMP is introduced as the property denoting one’s ability to compensate the errors in quality characteristics of the workpiece, given the allocation and character of measurements and controllable tooling. The notions of “within-station” and “between-station” compensability are also introduced to describe the ability to compensate upstream product errors within a given operation or between arbitrarily selected operations, respectively. The previous research also failed to concurrently utilize the historical and on-line measurements of product key characteristics for active model-based quality control. This dissertation will explore the possibilities of merging the well-known Run-to-Run (RtR) quality control methods with the model-based feed-forward process control methods. The novel method is applied to the problem of control of multi-layer overlay errors in lithography processes in semiconductor manufacturing. In this work, we first devised a multi-layer overlay model to describe the introduction and flow of overlay errors from one layer to the next, which was then used to pursue a unified approach to RtR and feedforward compensation of overlay errors in the wafer. At last, we extended the existing methodologies by considering inaccurately indentified noise characteristics in the underlying error flow model. This is also a very common situation, since noise characteristics are rarely known with absolute accuracy. We formulated the uncertainty in process noise characteristics using Linear Fractional Transformation (LFT) representation and solved the problem by deriving a robust control law that guaranties the product quality even under the worst case scenario of parametric uncertainties. Theoretical results have been evaluated and demonstrated using a linear state-space model of an actual industrial process for automotive cylinder head machining.