Many-to-many comprehensive relative importance analysis and its applications to analysis of semiconductor electrical testing parameters. (April 2021) - Shen, Zixin; Hong, Amos; Chen, Argon
Abstract: Most engineering systems have multiple inputs and multiple outputs. For example, a semiconductor manufacturing system consists of thousands of fabrication steps with numerous inline production parameters affecting multiple electrical characteristics of final chips. Many-to-many analysis is thus needed to more effectively discover critical factors causing poor product qualities or a low production yield. Though methodologies of many-to-many correlation analysis have been proposed in the literature, difficulties arise, especially when there exist multicollinearity effects among features, to measure the relative importance of a feature's contribution. Relative weight analysis offers a general framework for determining the relative importance of features in multiple linear regression models. In this article, we propose a many-to-many comprehensive relative importance analysis based on canonical correlation analysis to effectively summarize the relationship between two sets of features. Simulation and actual semiconductor yield-analysis cases are used to show the proposed method, as compared to other conventional methods, in analysis of two sets of features. Highlights: Motivated by the semiconductor yield problem, we investigate the MIMO systems. A many-to-many comprehensive relative important analysis method is proposed. Significant combined effects between two sets of variables can be effectively found. Various analysis methods are compared using simulation and real-world data.