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Analog circuit design optimization through the particle swarm optimization technique

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Abstract

This paper details the Particle Swarm Optimization (PSO) technique for the optimal design of analog circuits. It is shown the practical suitability of PSO to solve both mono-objective and multiobjective discrete optimization problems. Two application examples are presented: maximizing the voltage gain of a low noise amplifier for the UMTS standard and computing the Pareto front of a bi-objective problem, maximizing the high current cut off frequency and minimizing the parasitic input resistance of a second generation current conveyor. The aptness of PSO to optimize difficult circuit problems, in terms of numbers of parameters and constraints, is shown.

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Notes

  1. a (2 GHz, 2 Go RAM) core 2 DUO PC was used for this purpose.

References

  1. Gielen, G., & Sansen, W. (1991). Symbolic analysis for automated design of analog integrated circuits. Dordrecht: Kluwer Academic Publishers.

  2. Toumazou, C., Lidgey, F. J., & Haigh, D. G. (1993). Analog integrated circuits: The current mode approach, IEEE circuit and systems series 2.

  3. Graeb, H., Zizala, S., Eckmueller, J., & Antreich, K. (2001). The sizing rules method for analog integrated circuit design. IEEE/ACM International conference on computer-aided design, ICCAD’01. November 4–8, 2001. San Jose, CA, USA.

  4. Conn, A. R., Coulman, P. K., Haring, R. A. Morrill, G. L., & Visweswariah, C. (1996). Optimization of custom MOS circuits by transistor sizing. The international conference on computer aided design, ICCAD’96. November 10–14, 1996. San Jose, CA, USA.

  5. Medeiro, F., Rodríguez-Macías, R., Fernández, F. V., Domínguez-Astro, R., Huertas, J. L., & Rodríguez-Vázquez, A. (1994). Global design of analog cells using statistical optimization techniques. Analog integrated circuits and signal processing, 6(3), 179–195.

    Article  Google Scholar 

  6. Silveira, F., Flandre, D., & Jespers, P. G. A. (1996). A gm/Id based methodology for the design of CMOS analog circuits and its application to the synthesis of a SOI micropower OTA. IEEE Journal of solid state circuits, 31(9), 1314–1319.

    Article  Google Scholar 

  7. O’connor, I., & Kaiser, A. (2000). Automated synthesis of current memory cells. IEEE Transactions on computer aided design of integrated circuits and systems, 19(4), 413–424.

    Article  Google Scholar 

  8. Loulou, M., Ait Ali, S., Fakhfakh, M., & Masmoudi, N. (2002). An optimized methodology to design CMOS operational amplifier. IEEE International conference on microelectronic, ICM’2002, December 14–16, 2002. Beirut, Lebanon.

  9. Talbi, E. G. (2002). A taxonomy of hybrid metaheuristics. Journal of Heuristics, 8, 541–564.

    Article  Google Scholar 

  10. Aarts, E., & Lenstra, K. (2003). Local search in combinatorial optimization. Princeton: Princeton University Press.

  11. Dréo, J., Pétrowski, A., Siarry, P., & Taillard, E. (2006). Metaheuristics for hard optimization: Methods and case studies, New York: Springer.

  12. Siarry, P., Berthiau, G., Durbin, F., & Haussy, J. (1997). Enhanced simulated annealing for globally minimizing functions of many-continuous variables. ACM Transactions on Mathematical Software, 23, 209–228.

    Article  MATH  MathSciNet  Google Scholar 

  13. Kirkpatrick, S., Gelatt, C. D., & Vecchi, M. P. (1983). Optimization by simulated annealing. Journal of science, 220, 671–680.

    Article  MathSciNet  Google Scholar 

  14. Courat, J. P., Raynaud, G., Mrad, I., & Siarry, P. (1994). Electronic component model minimization based on log simulated annealing. IEEE Transactions on circuits and systems, 41, 790–795.

    Article  Google Scholar 

  15. Glover, F. (1989). Tabu search-part I. ORSA Journal on computing, 1(3), 190–206.

    MATH  Google Scholar 

  16. Glover, F. (1990). Tabu search-part II. ORSA Journal on computing., 2(1), 4–32.

    MATH  Google Scholar 

  17. Laguna, M., & Martí, R. (2003). Scatter search: Methodology and implementation in C, (Vol. 24). Series Operations Research/Computer Science Interfaces Series. New York: Springer.

  18. Grimbleby, J. B. (2000). Automatic analogue circuit synthesis using genetic algorithms. IEE Proceedings-Circuits, Devices and Systems, 147(6), 319–323.

    Article  Google Scholar 

  19. Dinger, R. H. (1998). Engineering design optimization with genetic algorithm. IEEE Northcon conference. October 21–23, 1998. Seattle WA, USA.

  20. Marseguerra, M., & Zio, E. (2000). System design optimization by genetic algorithms, The IEEE annual reliability and maintainability symposium, January 24–27, 2000. Los Angeles, California, USA.

  21. Dorigo, M., DiCaro, G., & Gambardella, L. M. (1999). Ant algorithms for discrete optimization. Artificial Life Journal, 5, 137–172.

    Article  Google Scholar 

  22. Chan, F. T. S., & Tiwari, M. K. (2007). Swarm Intelligence: focus on ant and particle swarm optimization. Vienna: I-Tech Education and Publishing.

  23. Kennedy, J., & Eberhart, R. C. (1995). Particle swarm optimization. The IEEE International conference on neural networks, November 27–December 1, 1995. WA, Australia.

  24. Clerc, M. (2006). Particle swarm optimization, International scientific and technical encyclopaedia.

  25. Maulik, P. C., & Carley, R. (1991). Automating analog circuit design using constrained optimization techniques. The IEEE international conference on computer-aided design. ICCAD’91. November 11–14, 1991. Santa Clara, CA, USA.

  26. Siarry, P., & Michalewicz, Z. (2007). Advances in metaheuristics for hard optimization. New York: Springer.

  27. Nelder, J. A., & Mead, R. (1965). A simplex method for function optimization. Computer Journal, 7, 308–313.

    MATH  Google Scholar 

  28. Doig, A. G., & Land, A. H. (1960). An automatic method for solving discrete programming problem. Econometrica, 28, 497.

    Article  MATH  MathSciNet  Google Scholar 

  29. Scniederjans, M. J. (1995). Goal programming methodology and applications. Dordrecht: Kluwer Publishers.

  30. Bellman, R. E. (2003). Dynamic programming. New York: Dover publication.

  31. Basseur, M., Talbi, E. G., Nebro, A., & Alba, E. (2006). Metaheuristics for multiobjective combinatorial optimization problems: review and recent issues, Report no. 5978. National institute of research in informatics and control (INRIA), September 2006.

  32. Yoshida, H., Kawata, K., Fukuyama, Y., Takayama, S., & Nakanish, Y. (2001). A particle swarm optimization for reactive power and voltage control considering voltage security assessment. The IEEE Transactions on Power Systems, 15(4), 1232–1239.

    Article  Google Scholar 

  33. Clerc, M., & Kennedy, J. (2002). The particle swarm: explosion, stability, and convergence in a multi-dimensional complex space. The IEEE Transactions on evolutionary computation, 6, 58–73.

    Article  Google Scholar 

  34. Reyes-Sierra, M., & Coello-Coello, C. A. (2006). Multi-objective particle swarm optimizers: a survey of the state-of-the-art. International Journal of Computational Intelligence Research, 2(3), 287–308.

    MathSciNet  Google Scholar 

  35. Fakhfakh, M., Loulou, M., & Masmoudi, N. (2007). Optimizing performances of switched current memory cells through a heuristic. Analog Integrated Circuits and Signal Processing, 50(2), 115–126.

    Article  Google Scholar 

  36. Fakhfakh, M. (2009). A novel alienor-based heuristic for the optimal design of analog circuits. Microelectronics Journal, 40(1), 141–148.

    Article  MathSciNet  Google Scholar 

  37. del Mar-Hershenson, M., Boyd, S., & Lee, T. H. (2001). CMOS operational amplifier design and optimization via geometric programming. The IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 20(1), 1–21.

    Article  Google Scholar 

  38. Zitzler, E., Laumanns, M., & Bleuler, S. (2004). A Tutorial on evolutionary multiobjective optimization. In X. Gandibleux, M. Sevaux, K. Sörensen & V. T’kindt (Eds.), Metaheuristics for multiobjective optimization. Lecture notes in economics and mathematical systems (Vol. 535, pp. 3–37). New York: Springer.

  39. Collette, Y., & Siarry, P. (2003). Multiobjective optimization—principles and case studies. New York: Springer.

  40. Bayart, B., Kotlicki, P., & Nowacki, M. (2002). Multiobjective optimization, Collection of reports for topics of evolutionary computation, Denmark.

  41. Raquel, C. R., & Naval, P. C. (2005). An effective use of distance in multiobjective particle swarm optimization. The genetic and evolutionary computation conference, GECCO’2005, June 25–29, 2005. Washington, DC, USA.

  42. Coello-Coello, C. A., & Lechuga, M. S. (2002). MOPSO: A proposal for multiple objective particle swarm optimization, The evolutionary computation congress. May 12–17, 2002. Hawaii.

  43. Razavi, B. (1998). RF Microelectronics. Englewood cliffs: Prentice Hall press.

  44. Andreani, P., & Sjoland, H. (2001). Noise optimization of an inductively degenerated CMOS low noise amplifier. The IEEE Transactions on Circuits and Systems II, 48(9), 835–841.

    Article  Google Scholar 

  45. Smith, K. C., & Sedra, A. (1968). The current conveyor—a new circuit building block. Proceeding of the IEEE, 56(8), 1368–1369.

    Article  Google Scholar 

  46. Sedra, A., & Smith, K. C. (1970). A second generation current conveyor and its applications. IEEE Transactions on Circuit Theory, 17(1), 132–134.

    Article  Google Scholar 

  47. Cooren, Y., Fakhfakh, M., Loulou, M., & Siarry, P. (2007). Optimizing second generation current conveyors using particle swarm optimization. The IEEE International conference on microelectronics, ICM’2007, December 29–31, 2007. Cairo, Egypt.

  48. Ben Salem, S., Fakhfakh, M., Masmoudi, D. S., Loulou, M., Loumeau, P., & Masmoudi, N. (2006). A High Performances CMOS CCII and High Frequency Applications. Analog Integrated Circuits and Signal Processing, 49(1), 71–78.

    Article  Google Scholar 

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Authors and Affiliations

  1. University of Sfax, B.P. 1069, Sfax, 3018, Tunisia

    Mourad Fakhfakh, Amin Sallem & Mourad Loulou

  2. University of Paris 12, Creteil Cedex, France

    Yann Cooren & Patrick Siarry

Authors
  1. Mourad Fakhfakh
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  2. Yann Cooren
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  3. Amin Sallem
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  4. Mourad Loulou
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  5. Patrick Siarry
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Correspondence to Mourad Fakhfakh.

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Fakhfakh, M., Cooren, Y., Sallem, A. et al. Analog circuit design optimization through the particle swarm optimization technique. Analog Integr Circ Sig Process 63, 71–82 (2010). https://doi.org/10.1007/s10470-009-9361-3

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