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• W3140594868 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." @default.
• W3140594868 created "2021-04-13" @default.
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• W3140594868 date "2021-04-01" @default.
• W3140594868 modified "2023-09-26" @default.
• W3140594868 title "Many-to-many comprehensive relative importance analysis and its applications to analysis of semiconductor electrical testing parameters" @default.
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• W3140594868 doi "https://doi.org/10.1016/j.aei.2021.101283" @default.
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