Generalized Linear Models and Process Variation

Abstract

Several approaches have been proposed for optimizing both the mean and variation of a process simultaneously. This paper reviews some of these methods and studies ways in which generalized linear models can be adapted for use with them. Specifically, a generalized linear model with gamma error distribution and log link function is used to model variation as (1) part of a screening method for variance control factors and (2) part of an algorithm for simultaneous maximum likelihood estimation of mean and variance parameters. The advantages and disadvantages of these two approaches are examined in detail and compared to other current methods.

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