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Today's high-tech industries produce complicated products involving many processing steps. The usual approach of modeling and controlling each of these steps in isolation is re-evaluated. This work develops a data model of synchronized observations collected from several stages of a multistage manufacturing process, and proposes an across-stage automatic control scheme for adjusting nonstationary process drifts. The proposed controller applies dynamic programming tools to optimize multiple goals specified for individual process stages and possible mismatch between stages. Several examples and simulation studies demonstrate that the proposed method is a valuable tool for improving semiconductor manufacturing quality.