Progress In Electromagnetics Research
ISSN: 1070-4698, E-ISSN: 1559-8985
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By M. Larbi, I. S. Stievano, F. Canavero, and P. Besnier

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This paper deals with uncertainty propagation applied to the analysis of crosstalk in printed circuit board microstrip traces. Complex interconnection networks generally are affected by many uncertain parameters and their point-to-point transfer functions are computationally expensive, thus making Monte-Carlo analyses rather inefficient. To overcome this situation, a metamodel is highly desirable. This paper presents a sparse and accelerated polynomial chaos approach, which proves to be well adapted for high-dimensional uncertainty quantification and well suited for the sensitivity analysis of crosstalk effects. We highlight the significant advantage of the advocated approach for the design of microstrip line networks of complex topology. In fact, we demonstrate how a small number of system simulations can help to quantify the statistics of the output variability and identify a reduced set of high-impact parameters.

M. Larbi, I. S. Stievano, F. Canavero, and P. Besnier, "Identfication of Main Factors of Uncertainty in a Microstrip Line Network," Progress In Electromagnetics Research, Vol. 162, 61-72, 2018.

1. Rong, A. and A. C. Cangellaris, "Interconnect transient simulation in the presence of layout and routing uncertainty," 2011 IEEE 20th Conference on Electrical Performance of Electronic Packaging and Systems, 157-160, Oct. 2011.

2. Prasad, A. K., M. Ahadi, B. S. Thakur, and S. Roy, "Accurate polynomial chaos expansion for variability analysis using optimal design of experiments," 2015 IEEE MTT-S International Conference on Numerical Electromagnetic and Multiphysics Modeling and Optimization (NEMO), 1-4, Aug. 2015.

3. Ginste, D. V., D. D. Zutter, D. Deschrijver, T. Dhaene, P. Manfredi, and F. Canavero, "Stochastic modeling-based variability analysis of on-chip interconnects," IEEE Transactions on Components, Packaging and Manufacturing Technology, Vol. 2, No. 7, 1182-1192, Jul. 2012.

4. Biondi, A., P. Manfredi, D. V. Ginste, D. D. Zutter, and F. G. Canavero, "Variability analysis of interconnect structures including general nonlinear elements in spice-type framework," Electronics Letters, Vol. 50, No. 4, 263-265, Feb. 2014.

5. Pham, T. A., E. Gad, M. S. Nakhla, and R. Achar, "Decoupled polynomial chaos and its applications to statistical analysis of high-speed interconnects," IEEE Transactions on Components, Packaging and Manufacturing Technology, Vol. 4, No. 10, 1634-1647, Oct. 2014.

6. Prasad, A. K. and S. Roy, "Global sensitivity based dimension reduction for fast variability analysis of nonlinear circuits," 2015 IEEE 24th Electrical Performance of Electronic Packaging and Systems (EPEPS), 97-100, Oct. 2015.

7. Zhang, Z., T. A. El-Moselhy, I. M. Elfadel, and L. Daniel, "Calculation of generalized polynomialchaos basis functions and gauss quadrature rules in hierarchical uncertainty quantification," IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, Vol. 33, No. 5, 728-740, May 2014.

8. Zhang, Z., T. W. Weng, and L. Daniel, "Big-data tensor recovery for high-dimensional uncertainty quantification of process variations," IEEE Transactions on Components, Packaging and Manufacturing Technology, Vol. 7, No. 5, 687-697, May 2017.

9. Prasad, A. K. and S. Roy, "Accurate reduced dimensional polynomial chaos for efficient uncertainty quantification of microwave/RF networks," IEEE Transactions on Microwave Theory and Techniques, Vol. 65, No. 10, 3697-3708, 2017.

10. Blatman, G. and B. Sudret, "Adaptive sparse polynomial chaos expansion based on least angle regression," Journal of Computational Physics, Vol. 230, No. 6, 2345-2367, 2011.

11. Larbi, M., I. S. Stievano, F. G. Canavero, and P. Besnier, "Variability impact of many design parameters: The case of a realistic electronic link," IEEE Transactions on Electromagnetic Compatibility, Vol. 60, No. 1, 34-41, Feb. 2018.

12. Soize, C. and R. Ghanem, "Physical systems with random uncertainties: Chaos representations with arbitrary probability measure," SIAM Journal on Scientific Computing, Vol. 26, No. 2, 395-410, 2004.

13. Berveiller, M., B. Sudret, and M. Lemaire, "Stochastic finite element: A non intrusive approach by regression," European Journal of Computational Mechanics, Vol. 15, No. 1-3, 81-92, 2006.

14. Montgomery, D. C., Design and Analysis of Experiments, John Wiley & Sons, New York, 2004.

15. Efron, B., T. Hastie, I. Johnstone, and R. Tibshirani, "Least angle regression," The Annals of Statistics, Vol. 32, No. 2, 407-499, 2004.

16. Sobol, I. M., "Sensitivity estimates for nonlinear mathematical models," Mathematical Modelling and Computational Experiments, Vol. 1, No. 4, 407-414, 1993.

17. Sudret, B., "Global sensitivity analysis using polynomial chaos expansions," Reliability Engineering & System Safety, Vol. 93, No. 7, 964-979, 2008.

18. Tang, T. K. and M. S. Nakhla, "Analysis of high-speed VLSI interconnects using the asymptotic waveform evaluation technique," IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, Vol. 11, No. 3, 341-352, Mar. 1992.

19. Marelli, S. and B. Sudret, "UQLab: A framework for uncertainty quantification in matlab," Proc. 2nd Int. Conf. on Vulnerability Risk Analysis and Management, 2554-2563, Liverpool, 2014.

20. McKay, M. D., R. J. Beckman, and W. J. Conover, "A comparison of three methods for selecting values of input variables in the analysis of output from a computer code," Technometrics, Vol. 42, No. 1, 55-61, 2000.

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