Shen ZHong AARGON CHEN2021-08-052021-08-05202114740346https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103341029&doi=10.1016%2fj.aei.2021.101283&partnerID=40&md5=542fffe992699e03d8d6e061d0d5c710https://scholars.lib.ntu.edu.tw/handle/123456789/577102Most 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. ? 2021 Elsevier LtdCorrelation methods; Electric network parameters; Linear regression; Semiconductor device manufacture; Canonical correlation analysis; Conventional methods; Correlation analysis; Electrical characteristic; Multiple inputs and multiple outputs; Multiple linear regression models; Production parameters; Semiconductor manufacturing systems; Quality controljournal article10.1016/j.aei.2021.1012832-s2.0-85103341029