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Analogue Circuit Optimization through a Hybrid Approach

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Intelligent Computational Optimization in Engineering

Part of the book series: Studies in Computational Intelligence ((SCI,volume 366))

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Abstract

Optimal analogue circuit sizing is investigated in this chapter. It is shown that hybridization of a global optimization approach with a local one leads to better results in optimization of such circuits, than using classical approaches. The case of merging Genetic Algorithms with the Simulated Annealing technique is considered. The hybrid algorithm is detailed and is evaluated using test functions. It is shown through three application examples, i.e. optimization of performances of a current conveyor, an operational transconductance amplifier and a low noise amplifier, that such hybrid algorithms yield optimal solutions in a much shorter time, when compared to conventional meta-heuristics.

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References

  1. Gielen, G., Sansen, W.: Symbolic Analysis for Automated Design of Analog Integrated Circuits. Kluwer Academic Publishers, Dordrecht (1991)

    Book  Google Scholar 

  2. Fakhfakh, M., Tlelo-Cuautle, E., Fernández, F.V.: Design of Analog Circuits through Symbolic Analysis. Bentham Scientific Publisher (2010); eISBN: 978-1-60805-095-6

    Google Scholar 

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

    Google Scholar 

  4. Roca, E., Fakhfakh, M., Castro-López, R., Fernández, F.V.: Applications of Evolutionary Computation Techniques to Analog, Mixed-Signal and RF Circuit Design – An Overview. In: The IEEE International Conference on Electronics, Circuits, and Systems, ICECS, Tunisia (2009)

    Google Scholar 

  5. Tlelo-Cuautle, E., Duarte-Villasenor, M.A.: Evolutionary electronics: automatic synthesis of analog circuits by GAs. In: Yang Ang, B.L.T., Yin, S. (eds.) Success in Evolutionary Computation. SCI, pp. 165–188. IGI Global (2008)

    Google Scholar 

  6. Liu, B., Fernández, F.V., Gielen, G., Castro-Lopez, R., Roca, E.: A Memetic Approach to the Automatic Design of High-Performance Analog Integrated Circuits. ACM Trans on Design Automation of Electronic Systems 14(3) Article 42 (2009)

    Google Scholar 

  7. Medeiro, F., Rodríguez-Macías, R., Fernández, F.V., Domínguez-Castro, R., Huertas, J.L., Rodríguez-Vázquez, A.: Global Design of Analog Cells Using Statistical Optimization Techniques. Analog integrated circuits and signal processing 6, 179–195 (1994)

    Article  Google Scholar 

  8. Fakhfakh, M., Loulou, M., Masmoudi, N.: A Novel Heuristic for Multi-Objective Optimization of Analog Circuit Performances. Analog Integrated Circuits & Signal Processing 61(1), 47–64 (2009)

    Article  Google Scholar 

  9. Fakhfakh, M.: A Novel Alienor-Based Heuristic for the Optimal Design of Analog Circuits. Microelectronics Journal 40(1), 141–148 (2009)

    Article  MathSciNet  Google Scholar 

  10. Talbi, E.G.: A Taxonomy of Hybrid Metaheuristics. Journal of Heuristics 8(5), 541–564 (2002)

    Article  Google Scholar 

  11. Siarry, P., Michalewicz, Z.: Advances in Metaheuristics for Hard Optimization, pp. 1619–7127. Springer, Heidelberg (2007); ISSN: 1619-7127

    Google Scholar 

  12. Abraham, A., Hassanien, A.E., Siarry, P., Engelbrecht, A.: Foundations of Computational Intelligence Volume 3: Global Optimization. Springer, Heidelberg (2009); ISSN:1860949X

    MATH  Google Scholar 

  13. Dréo, J., Petrowski, A., Siarry, P., Taillard, E.: Metaheuristics for Hard Optimization: methods and case studies. Springer, Heidelberg (2006); ISBN: 978-3-540-23022-9

    MATH  Google Scholar 

  14. Daniel, A.: Evolutionary Computation for Modeling and Optimization. Springer, Heidelberg (2006); ISBN: 978-0-387-22196-0

    MATH  Google Scholar 

  15. Eiben, A.E., Smith, J.E.: Introduction to evolutionary computing. Springer, Heidelberg (2007); ISBN: 978-3-540-40184-1

    Google Scholar 

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

    Article  Google Scholar 

  17. Dinger, R.H.: Engineering design optimization with genetic algorithm. In: IEEE Northcon Conference, Seattle WA, USA (1998)

    Google Scholar 

  18. Marseguerra, M., Zio, E.: System design optimization by genetic algorithms. In: The IEEE Annual Reliability and Maintainability Symposium, Los Angeles, California USA (2000)

    Google Scholar 

  19. Fogel, L., Owers, A., Walsh, M.: Artificial intelligence through simulated evolution. Wiley, Chichester (1996); ISBN-13: 978-0471265160

    Google Scholar 

  20. Chan, F.T.S., Tiwari, M.K.: Swarm Intelligence: focus on ant and particle swarm optimization. I-Tech Education and Publishing (2007); ISBN 978-3-902613-09-7

    Google Scholar 

  21. Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In: Proceedings of the IEEE International Conference On Neural Networks, pp. 1942–1948.

    Google Scholar 

  22. Clerc, M.: Particle swarm optimization. In: International Scientific and Technical Encyclopedia (2006); ISBN-10: 1905209045

    Google Scholar 

  23. Dorigo, M., Di-Caro, G., Gambardella, L.M.: Ant algorithms for discrete optimization. Artificial life Journal 5, 137–172 (1999)

    Article  Google Scholar 

  24. Passino, K.M.: Biomimicry of bacterial foraging for distributed optimization and control. IEEE Control Systems Magazine, 52–67 (2002)

    Google Scholar 

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

    Article  MATH  Google Scholar 

  26. Kirkpatrick, S., Gelatt, C.D., Vecchi, M.P.: Optimization by simulated annealing. Journal of Science 220, 671-220-680 (1983)

    Google Scholar 

  27. Courat, J.P., Raynaud, G., Mrad, I., Siarry, P.: Electronic component model minimization based on Log Simulated Annealing. IEEE Transactions on Circuits and Systems 41, 790–795 (1994)

    Article  Google Scholar 

  28. Durbin, F., Haussy, J., Berthiau, G., Siarry, P.: Circuit performance optimization and model fitting based on simulated annealing. International Journal of Electronics 73, 1267–1271 (1992)

    Article  Google Scholar 

  29. Glover, F.: Tabu search- part I. ORSA Journal on Computing 1(3) Summer (1989)

    Google Scholar 

  30. Glover, F.: Tabu search- part II. ORSA Journal on Computing 2(1) Winter (1990)

    Google Scholar 

  31. Fakhfakh, M., Cooren, Y., Sallem, A., Loulou, M., Siarry, P.: Analog Circuit Design Optimization through the Particle Swarm Optimization Technique. Analog Integrated Circuits & Signal Processing 63(1), 71–82 (2009)

    Article  Google Scholar 

  32. Tlelo-Cuautle, E., Duarte-Villasenor, M.A., Guerra-Gomez, I.: Automatic synthesis of VFs and VMs by applying genetic algorithms. Circuits, Systems, Signal Processing 27, 391–403 (2008)

    Article  Google Scholar 

  33. Tlelo-Cuautle, E., Guerra-Gomez, I., Reyes-Garcıa, C.A., Duarte-Villasenor, M.A.: Synthesis of Analog Circuits by Genetic Algorithms and their Optimization by Particle Swarm Optimization. In: Chiong, R. (ed.) Intelligent Systems for Automated Learning and Adaptation: Emerging Trends and Applications, pp. 173–192. IGI Global (2010), doi: 10.4018/978-1-60566-798-0. ch008

    Google Scholar 

  34. Chu, M., Allstot, D.J.: Elitist nondominated sorting genetic algorithm based RF IC optimizer. IEEE Transactions on Circuits and Systems – I 52, 535–545 (2005)

    Article  MathSciNet  Google Scholar 

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

    Article  Google Scholar 

  36. Maitra, M., Chatterjee, A.: A novel technique for multilevel optimal magnetic resonance brain image thresholding using bacterial foraging. Elsevier B.V. 41, 1124–1134 (2008)

    Google Scholar 

  37. Metropolis, N., Rosenbluth, A.W., Rosenbluth, M.N., Teller, A.H., Teller, E.: Equation of state calculations by fast computing machines. Journal of Chemical Physics 6(21), 1087–1092 (1953)

    Article  Google Scholar 

  38. Kerlner, V., et al.: A hybrid optimization technique coupling an evolutionary and a local search algorithm. Journal of Computational and Applied Mathematics 215, 448–456 (2008); ISSN:0377-0427

    Article  MathSciNet  Google Scholar 

  39. Crina, G., Ajith, A., Hisao, I.: Hybrid Evolutionary Algorithms. Springer, Heidelberg (2007)

    MATH  Google Scholar 

  40. El-Hosseini, M.A., Hassanien, A.E., Ajith, A., Al-Qaheri, A.: Genetic Annealing Optimization: Design and Real World Applications. In: The International Conference on Intelligent Systems Design and Applications, Kaohsiung, Taiwan (2008)

    Google Scholar 

  41. Kurbel, K., Schneider, B., Singh, K.: Solving optimization problems by parallel recombinative simulated annealing on a parallel computer-an application to standard cell placement in VLSI design. IEEE Transactions on Systems, Man and Cybernetics, Part B 28(3), 454–461 (1998)

    Article  Google Scholar 

  42. Zhang, L., Raut, R., Jiang, Y., Kleine, U.: Placement algorithm in analog-layout designs. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 25(10), 1889–1903 (2006)

    Article  Google Scholar 

  43. Somani, A., Chakrabarti, P.P., Patra, A.: Mixing Global and Local Competition in Genetic Optimization based Design Space Exploration of Analog Circuits. In: The Design, Automation and Test in Europe Conference, Munich, Germany (2005)

    Google Scholar 

  44. Esbensen, H., Mazumder, P.: SAGA: A unification of the genetic algorithm with simulated annealing and its application to macro-cell placement. In: The International Conference on VLSI Design, Calcutta, India (1994)

    Google Scholar 

  45. Holland, J.H.: Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor (1975)

    Google Scholar 

  46. Weise, T.: Global optimization algorithms – theory and applications (2009), http://www.it-weise.de

  47. Haupt, R.L., Haupt, S.E.: Practical Genetic Algorithms. John Wiley & Sons, Chichester (2004); ISBN 0-471-45565-2

    MATH  Google Scholar 

  48. Rutenber, R.A.: Simulated Annealing Algorithms: An Overview. IEEE Circuits and Devices Magazine 5(1), 19–26 (1989)

    Article  Google Scholar 

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

    Article  Google Scholar 

  50. Razavi, B.: Design of Analog CMOS Integrated Circuit. TheMcGraw-Hill Companies, Inc., United States (2001); ISBN:0-07-118815-0

    Google Scholar 

  51. Berntsen, O., Wulff, C., Ytterdal, T.: High Speed, High Gain OTA in a Digital 90 nm CMOS Technology. In: NORCHIP Conference, pp. 129–132 (2005)

    Google Scholar 

  52. Phillip, E., Allen, D., Holberg, R.: CMOS Analog Circuit Design. Oxford University Press, Inc., Oxford (2002)

    Google Scholar 

  53. Zhang, L., Kim, H.J., Nadig, V., Ismail, M.: A 1.8 V tri-mode ΣΔ modulator for GSM/WCDMA/WLAN wireless receiver. Analog Integrated Circuits and Signal Processing 49(3), 323–341 (2006)

    Article  Google Scholar 

  54. Daoud, H., Bennour, S., Ben-Salem, S., Loulou, M.: Low power SC CMFB folded cascode OTA optimization. In: The IEEE International Conference on Electronics, Circuits and Systems, ICECS, Malta (2008)

    Google Scholar 

  55. Daoud, H., Ben-Salem, S., Zouari, S., Loulou, M.: Design of folded cascode OTA in different regions of operation through gm/ID methodology. The world academy of science, engineering and technology 45, 28–33 (2008)

    Google Scholar 

  56. Razavi, B.: RF Microelectronics. Prentice Hall press, Englewood Cliffs (1998)

    Google Scholar 

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

    Article  Google Scholar 

  58. Ellinger, F.: Radio frequency integrated circuits and technologies. Springer, Heidelberg (2007)

    Google Scholar 

  59. Kurokawa, K.: Power waves and the scattering matrix. IEEE Transactions on Microwave Theory and Techniques 13(2), 194–202 (1965)

    Article  Google Scholar 

  60. Kurokawa, K.: An introduction to the theory of microwave circuits. Academic Press, London (1969)

    Google Scholar 

  61. Scott, A.W.: Understanding Microwaves.TK7876.S36. John Wiley & Sons. Inc., Chichester (1993) ISBN 0-471-57567-4

    Google Scholar 

  62. Heng, Z., Xiaohua, F., Sánchez, S.E.: A Low-Power, Linearized, Ultra-Wideband LNA Design Technique. IEEE Journal of solid-state circuits 44(2), 320–339 (2009)

    Article  Google Scholar 

  63. Fakhfakh, M., Loulou, M.: A Software for the Automated Computing of Symbolic Transfer Functions of Analog Circuits. In: The International Workshop on Symbolic and Numerical Methods, Modeling and Applications to Circuit Design, Erfurt, Germany (2008)

    Google Scholar 

  64. Fakhfakh, M., Loulou, M.: Live Demonstration: CASCADES.1: a Flow-Graph-Based Symbolic Analyzer. In: The IEEE International Symposium on Circuits and Systems, ISCAS, Paris, France (2010)

    Google Scholar 

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Fakhfakh, M. et al. (2011). Analogue Circuit Optimization through a Hybrid Approach. In: Köppen, M., Schaefer, G., Abraham, A. (eds) Intelligent Computational Optimization in Engineering. Studies in Computational Intelligence, vol 366. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21705-0_11

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  • DOI: https://doi.org/10.1007/978-3-642-21705-0_11

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