Multi-Objective Optimization of RF Circuit Blocks via Surrogate Models and NBI and SPEA2 Methods

  • Luciano De Tommasi
  • Theo G. J. Beelen
  • Marcel F. Sevat
  • Joost Rommes
  • E. Jan W. ter Maten
Conference paper
Part of the Mathematics in Industry book series (MATHINDUSTRY, volume 17)

Abstract

Multi-objective optimization techniques can be categorized globally into deterministic and evolutionary methods. Examples of such methods are the Normal Boundary Intersection (NBI) method and the Strength Pareto Evolutionary Algorithm (SPEA2), respectively. With both methods one explores trade-offs between conflicting performances. Surrogate models can replace expensive circuit simulations so enabling faster computation of circuit performances. As surrogate models of behavioral parameters and performance outcomes, we consider look-up tables with interpolation and Neural Network models.

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References

  1. 1.
    Beelen, T.G.J., ter Maten, E.J.W., Sihaloho, H.J., van Eijndhoven, S.J.L.: Behavioral modeling of the dominant dynamics in input-output transfer of linear(ized) circuits. Procedia Comp. Sci. 1(1), 347–355 (2010)CrossRefGoogle Scholar
  2. 2.
  3. 3.
    Cioppa, T.M., Lucas, T.W.: Efficient nearly orthogonal and space-filling Latin Hypercubes. Technometrics 49-1, 45–55 (2007)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Das, I., Dennis, J.E.: Normal-Boundary Intersection: A new method for generating Pareto optimal points in multicriteria optimization problems. SIAM J. Optim. 8-3, 631–657 (1998)MathSciNetCrossRefGoogle Scholar
  5. 5.
  6. 6.
    De Tommasi, L., Gorissen, D., Croon, J., Dhaene, T.: Surrogate modeling of low noise amplifiers based on transistor level simulations. In: Roos, J., Costa, R.J. (eds.) Scientific Computing in Electrical Engineering SCEE 2008. Mathematics in Industry, vol. 14, pp. 225–232. Springer, Berlin (2010)CrossRefGoogle Scholar
  7. 7.
    De Tommasi, L., Gorrisen, D., Croon, J.A., Dhaene, T.: Surrogate modeling of RF circuit blocks. In: Fitt, A.D., Norbury, J., Ockendon, H., Wilsson, E. (eds.) Progress in industrial mathematics at ECMI 2008. Mathematics in industry, vol. 15, pp. 447–452. Springer, Berlin (2010)CrossRefGoogle Scholar
  8. 8.
    De Tommasi, L., Rommes, J., Beelen, T., Sevat, M., Croon, J.A., Dhaene, T.: Forward and reverse modeling of low noise amplifiers based on circuit simulations. In: Benner, P., Hinze, M., ter Maten, E.J.W. (eds.) Model Reduction for Circuit Simulation. Lecture Notes in Electrical Engineering, vol. 74, pp. 111–124. Springer, Berlin (2011)CrossRefGoogle Scholar
  9. 9.
    Finkel, D.E.: Global optimization with the direct algorithm. PhD-Thesis North Carolina State University (2005). http://pages.cs.wisc.edu/~ferris/cs726/direct.m
  10. 10.
    Stehr, G., Gräb, H.E., Antreich, K.J.: Analog performance space exploration by Normal-Boundary Intersection and by Fourier-Motzkin elimination. IEEE Trans. Comp.-Aided Des. Integrated Circ. Syst. 26-10, 1733–1745 (2007)CrossRefGoogle Scholar
  11. 11.
  12. 12.
    Zitzler, E., Laumanns, M., Thiele, L.: SPEA2: Improving the Strength Pareto Evolutionary Algorithm, Techn. Report TIK Report 103, ETH Zürich. http://www.tik.ee.ethz.ch/pisa/selectors/spea2/spea2_c_source.html (2001)

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Luciano De Tommasi
    • 1
  • Theo G. J. Beelen
    • 2
  • Marcel F. Sevat
    • 3
  • Joost Rommes
    • 2
  • E. Jan W. ter Maten
    • 4
    • 5
  1. 1.United Technologies Research CenterCorkIreland
  2. 2.NXP Semiconductors, Central R&DEindhovenThe Netherlands
  3. 3.LPDHeerlenThe Netherlands
  4. 4.Department of Mathematics and Computer ScienceEindhoven University of Technology, CASAEindhovenThe Netherlands
  5. 5.Bergische Universität Wuppertal, FB C, AMNAWuppertalGermany

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