Comparison of three different methods to model the semiconductor manufacturing yield

Author

Dupret, Yoan ; Perrin, Erwan ; Grolier, JeanLuc ; Kielbasa, Richard

Author_Institution

AItis Semicond., Corbeil-Essonnes

fYear

2005

fDate

11-12 April 2005

Firstpage

118

Lastpage

123

Abstract

During a semiconductor manufacturing process a large amount of data is stored in databases. These data can be used to model the semiconductor manufacturing yield. A model of the yield according to process measurements is useful to predict the yield before final tests. It is also an help to do sensitivity analysis of the yield to process variations. This paper compares three methods to model the manufacturing yield from test data. Principal components analysis, independent component analysis and partial least squares regression are reviewed. A methodology is then exposed to achieve, efficient manufacturing yield modeling

Keywords

independent component analysis; integrated circuit yield; least squares approximations; principal component analysis; production engineering computing; regression analysis; semiconductor process modelling; sensitivity analysis; independent component analysis; manufacturing yield modeling; partial least squares regression; principal components analysis; process measurements; semiconductor manufacturing process; Databases; Independent component analysis; Manufacturing processes; Predictive models; Principal component analysis; Pulp manufacturing; Semiconductor device manufacture; Sensitivity analysis; Testing; Virtual manufacturing;

fLanguage

English

Publisher

ieee

Conference_Titel

Advanced Semiconductor Manufacturing Conference and Workshop, 2005 IEEE/SEMI

Conference_Location

Munich

Print_ISBN

0-7803-8997-2

Type

conf

DOI

10.1109/ASMC.2005.1438778

Filename

1438778