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Hier finden Sie wissenschaftliche Publikationen aus den FraunhoferInstituten. A general approach for multivariate statistical MOSFET compact modeling preserving correlations
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Postprint urn:nbn:de:0011n1803694 (314 KByte PDF) MD5 Fingerprint: d0914c9e86e30e354ffad5305a0efdfd © 2011 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. Created on: 23.6.2012 
 Institute of Electrical and Electronics Engineers IEEE: 41st European SolidState Device Research Conference, ESSDERC 2011. Proceedings : Helsinki, September 12  16, 2011 Piscataway, NJ: IEEE Service Center, 2011 ISBN: 9781457707070 (Print) ISBN: 9781457707063 (Online) ISBN: 9781457707087 pp.163166 
 European SolidState Device Research Conference (ESSDERC) <41, 2011, Helsinki> 

 English 
 Conference Paper, Electronic Publication 
 Fraunhofer IIS, Institutsteil Entwurfsautomatisierung (EAS) () 
Abstract
As feature sizes shrink, random fluctuations gain importance in semiconductor manufacturing and integrated circuit design. Therefore, statistical device variability has to be considered in circuit design and analysis to properly estimate their impact and avoid expensive overdesign. Statistical MOSFET compact modeling is required to accurately capture marginal distributions of varying device parameters and to preserve their statistical correlations. Due to limited simulator capabilities, variables are often assumed to be normally distributed. Although correlations may be captured using Principal Component Analysis, such an assumption may be inaccurate. As an alternative, Nonlinear Power Models have been proposed. Since we see some limitations in this approach, we analyze whether the multivariate Generalized Lambda Distribution is an alternative for statistical device modeling. Applying both approaches to extracted statistical device parameters, we conclude that both methods do not differ significantly in accuracy, but the multivariate Generalized Lambda Distribution is more general and less computationally expensive.