Process Yield Analysis for Linear Within-Profile Autocorrelation

In many industrial applications, the quality of a process or product can be characterized by a function or profile. Owing to spatial autocorrelation or time collapse, the assumption of the observations within each profile that are uncorrelated is violated. This paper aims at evaluating the process yield for linear within-profile autocorrelation. We present an approximate lower confidence bound for S_pkA when the observations within each profile follow a first-order autoregressive AR(1) model. A simulation study is conducted to assess the performance of the proposed method. The simulation results confirm that the proposed method performs well for the bias, the standard deviation, and the coverage rate. One real example is used to demonstrate the applications of the proposed approach.