Many-to-many comprehensive relative importance analysis and its applications to analysis of semiconductor electrical testing parameters
Journal
Advanced Engineering Informatics
Journal Volume
48
Date Issued
2021
Author(s)
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. ? 2021 Elsevier Ltd
Subjects
Correlation 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 control
Type
journal article