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Using Sensitivities for Symbolic Analysis and Model Order Reduction of Systems with Parameter Variation

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Progress in Industrial Mathematics at ECMI 2010

Part of the book series: Mathematics in Industry ((TECMI,volume 17))

Abstract

The ongoing trend from micro- to nanoelectronics causes the growth of the relative parameter variation during the integrated electronic circuits production resulting in a consequent reduction of the production yield. Thus, symbolic model order reduction (MOR) techniques which were developed for design and analysis of nominal systems have to be adapted to assist the design of circuits which are robust with respect to parameter variation. Therefore, new sensitivity based methods have to be introduced to estimate the output of statistical systems and to improve the performance of the statistical MOR methods.

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References

  1. Analog Insydes: The intelligent symbolic design system for analog circuits. www.analog-insydes.com

  2. Buhmann, M.D.: Radial Basis Functions: Theory and Implementations. Cambridge University Press, Cambridge (2003)

    Google Scholar 

  3. Freedman, D., Pisani, R., Purves, R.: Statistics, 4th edn. W.W.Norton and Company, NY (2007)

    Google Scholar 

  4. Georgii, H.O., Ortgiese, M., Baake, E.: Stochastics: Introduction to Probability and Statistics. de Gruyter, NY (2008)

    Google Scholar 

  5. Halfmann, T., Broz, J., Knoth, C., Platte, D., Rotter, P.: Generation of efficient behavioral models using model compilation and model reduction techniques. In: Proceedings of Xth International Workshop on Symbolic and Numerical Methods, Modeling and Applications to Circuit Design. SMACD, Erfurt, Germany (2008)

    Google Scholar 

  6. Saltelli, A., Chan, K., Scott, E.M. (eds.): Sensitivity Analysis. Wiley Series in Probability and Statistics. Wiley, NY (2000)

    Google Scholar 

  7. Salzig, C., Hauser, M.: Design of robust electronic circuits for yield optimization. In: Proceedings of XIth International Workshop on Symbolic and Numerical Methods, Modeling and Applications to Circuit Design. SM2ACD, Tunis-Gammarth, Tunisia (2010)

    Google Scholar 

  8. Wichmann, T.: Symbolische Reduktionsverfahren für nichtlineare DAE-Systeme. Ph.D. thesis, Fraunhofer ITWM Kaiserslautern (2004)

    Google Scholar 

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Correspondence to Christian Salzig .

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© 2012 Springer-Verlag Berlin Heidelberg

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Salzig, C., Hauser, M., Venturi, A. (2012). Using Sensitivities for Symbolic Analysis and Model Order Reduction of Systems with Parameter Variation. In: Günther, M., Bartel, A., Brunk, M., Schöps, S., Striebel, M. (eds) Progress in Industrial Mathematics at ECMI 2010. Mathematics in Industry(), vol 17. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25100-9_22

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