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Monitoring Profiles Based on Nonparametric Regression Methods

Summary: [This abstract is based on the authors' abstract.] Statistical process control (SPC) problems with non-linear profiles are especially challenging because of the large amounts of data related to quality measurement now available. A methodology is proposed to monitor changes in both the regression relationship and the variation of the profile online. The methodology provides an effective SPC solution for nonlinear profiles, and also is able to detect changes due to a misspecified, out-of-control model. In addition, an approach is provided to locate the change point of the process and identify the type of change in the profile. An example from semiconductor manufacturing illustrates the application of the approach.

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  • Topics: Statistical Process Control (SPC)
  • Keywords: Exponentially weighted moving average (EWMA), Likelihood methods, Goodness of fit, Nonlinear models, Statistical process control (SPC), Regression analysis
  • Author: Zou, Changliang; T Sung, Fugee; Wang, Zhaojun
  • Journal: Technometrics