Dominance index for many-to-many correlation and its applicaions to semiconductor yield analysis
Journal
Winter Simulation Conference
Date Issued
2012
Author(s)
Hong, A.
Abstract
As more and more functionalities are packed into a single product, one-response-at–a-time correlation analysis is no longer sufficient to discover critical factors that result in poor qualities or a low yield. Though methodologies of many-to-many correlation analysis have been proposed in the literature, difficulties arise, especially when there exist multi-collinearity effects among variables, to measure the relative importance of a variable’s contribution in the association between a set of responses and a set of factors. Johnson’s dominance analysis (Johnson 2000) offers a general framework for determination of relative importance of independent variables in linear multiple regression models. In this article, we extend Johnson’s dominance index to many-to-many correlation analysis as a measurement to summarize the association relationship between two sets of variables. Actual semiconductor yield-analysis cases are used to illustrate the method and its effectiveness in analysis of two sets of variables. 1
SDGs
Type
conference paper