Skip to main content
SpringerLink
Log in
Book cover

Automated Design of Analog and High-frequency Circuits pp 19–40Cite as

Fundamentals of Optimization Techniques in Analog IC Sizing

  • Chapter
  • First Online:

Part of the book series: Studies in Computational Intelligence ((SCI,volume 501))

Abstract

Chapter 2 introduces the basics or fundamentals of evolutionary algorithms and constraint handling methods with the practical application of analog integrated circuit sizing. This chapter covers evolutionary algorithms for single and multiobjective optimization and basic constraint handling techniques. Popular methods are introduced with practical examples.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Notes

  1. 1.

    The maximization of a design objective can easily be transformed into a minimization problem by just inverting its sign.

  2. 2.

    Note that in the algorithmic description for multi-objective optimization, to avoid the confusion of indices in a vector and the indices in a group of vectors, a superscript \(i\) indicates the \(i\)th individual of a group, and a subscript \(i\) indicates the \(i\)th element in a vector.

References

  1. Nam D, Seo Y, Park L, Park C, Kim B (2001) Parameter optimization of an on-chip voltage reference circuit using evolutionary programming. IEEE Trans Evol Comput 5(4):414–421

    Article  Google Scholar 

  2. Martens E, Gielen G (2008) Classification of analog synthesis tools based on their architecture selection mechanisms. Integr VLSI J 41(2):238–252

    Article  Google Scholar 

  3. Krasnicki M, Phelps R, Hellums J, McClung M, Rutenbar R, Carley L (2001) ASF: a practical simulation-based methodology for the synthesis of custom analog circuits. In: Proceedings of IEEE/ACM international conference on computer aided design, pp 350–357

    Google Scholar 

  4. Degrauwe M, Nys O, Dijkstra E, Rijmenants J, Bitz S, Goffart L, Vittoz E, Cserveny S, Meixenberger C, Van Der Stappen G et al (1987) IDAC: an interactive design tool for analog CMOS circuits. IEEE J Solid-State Circuits 22(6):1106–1116

    Article  Google Scholar 

  5. Harjani R, Rutenbar R, Carley L (1989) OASYS: a framework for analog circuit synthesis. IEEE Trans Comput Aided Des Integr Circuits Syst 8(12):1247–1266

    Article  Google Scholar 

  6. Makris C, Toumazou C (1995) Analog IC design automation. ii. automated circuit correction by qualitative reasoning. IEEE Trans Comput Aided Des Integr Circuits Syst 14(2):239–254

    Google Scholar 

  7. Ochotta E, Rutenbar R, Carley L (1996) Synthesis of high-performance analog circuits in ASTRX/OBLX. IEEE Trans Comput Aided Des Integr Circuits Syst 15(3):273–294

    Article  Google Scholar 

  8. Gielen G, Walscharts H, Sansen W (1990) Analog circuit design optimization based on symbolic simulation and simulated annealing. IEEE J Solid-State Circuits 25(3):707–713

    Article  Google Scholar 

  9. Maulik P, Carley L, Allstot D (1993) Sizing of cell-level analog circuits using constrained optimization techniques. IEEE J Solid-State Circuits 28(3):233–241

    Article  Google Scholar 

  10. Boyd S, Lee T et al (2001) Optimal design of a CMOS op-amp via geometric programming. IEEE Trans Comput Aided Des Integr Circuits Syst 20(1):1–21

    Article  Google Scholar 

  11. Nye W, Riley D, Sangiovanni-Vincentelli A, Tits A (1988) DELIGHT. SPICE: an optimization-based system for the design of integrated circuits. IEEE Trans Comput Aided Des Integr Circuits Syst 7(4):501–519

    Google Scholar 

  12. Phelps R, Krasnicki M, Rutenbar R, Carley L, Hellums J (2000) Anaconda: simulation-based synthesis of analog circuits via stochastic pattern search. IEEE Trans Comput Aided Des Integr Circuits Syst 19(6):703–717

    Article  Google Scholar 

  13. Medeiro F, Rodríguez-Macías R, Fernández F, Domínguez-Castro R, Huertas J, Rodríguez-Vázquez A (1994) Global design of analog cells using statistical optimization techniques. Analog Integr Circuits Signal Process 6(3):179–195

    Google Scholar 

  14. Castro-López R, Fernández F, Guerra-Vinuesa O (2006) Re-use based methodologies and tools in the design of analog and mixed-signal integrated circuits. Springer, Berlin

    Google Scholar 

  15. Stehr G, Pronath M, Schenkel F, Graeb H, Antreich K (2003) Initial sizing of analog integrated circuits by centering within topology-given implicit specification. In: Proceedings of the IEEE/ACM international conference on computer-aided design, pp 241–246

    Google Scholar 

  16. Balkir S, Dundar G, Alpaydin G (2004) Evolution based synthesis of analog integrated circuits and systems. In: Proceedings of NASA/DoD conference on evolvable hardware, pp 26–29

    Google Scholar 

  17. Takemura K, Koide T, Mattausch H, Tsuji T (2004) Analog-circuit-component optimization with genetic algorithm. In: Proceedings of the 47th midwest symposium on circuits and systems, vol 1, pp 489–492

    Google Scholar 

  18. Barros M, Neves G, Guilherme J, Horta N (2005) An evolutionary optimization kernel with adaptive parameters applied to analog circuit design. In: Proceedings of international symposium on signals, circuits and systems, vol 2, pp 545–548

    Google Scholar 

  19. Goh C, Li Y (2001) GA automated design and synthesis of analog circuits with practical constraints. In: Proceedings of the congress on evolutionary computation, vol 1, pp 170–177

    Google Scholar 

  20. Koza J, Bennett F III (1997) Automated synthesis of analog electrical circuits by means of genetic programming. IEEE Trans Evol Comput 1(2):109–128

    Article  Google Scholar 

  21. Alpaydin G, Balkir S, Dundar G (2003) An evolutionary approach to automatic synthesis of high-performance analog integrated circuits. IEEE Trans Evol Comput 7(3):240–252

    Article  Google Scholar 

  22. Kruiskamp W, Leenaerts D (1995) DARWIN: CMOS opamp synthesis by means of a genetic algorithm. In: Proceedings of the 32nd annual ACM/IEEE design automation conference, pp 433–438

    Google Scholar 

  23. Antao B, Gielen G, Rutenbar R (2002) Computer-aided design of analog integrated circuits and systems. Wiley, USA

    Google Scholar 

  24. Synopsys (2013) HSPICE homepage. http://www.synopsys.com/Tools/Verification/AMSVerification/CircuitSimulation/HSPICE/Pages/default.aspx

  25. Storn R, Price K (1997) Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim 11(4):341–359

    Article  MathSciNet  MATH  Google Scholar 

  26. Price K, Storn R, Lampinen J (2005) Differential evolution: a practical approach to global optimization. Springer, New York

    Google Scholar 

  27. Michalewicz Z (1996) Genetic algorithms+ data structures. Springer, New York

    Google Scholar 

  28. Deb K, Agrawal R (1995) Simulated binary crossover for continuous search space. Complex Syst 9(2):115–148

    MathSciNet  MATH  Google Scholar 

  29. Poli R, Kennedy J, Blackwell T (2007) Particle swarm optimization. Swarm Intell 1(1):33–57

    Article  Google Scholar 

  30. Rechenberg I (1994) Evolution strategy. Computational intelligence: imitating. Life 1:147–159

    Google Scholar 

  31. Hansen N (2006) The CMA evolution strategy: a comparing review. Towards a new evolutionary computation. Springer, pp 75–102

    Google Scholar 

  32. Gregory M, Bayraktar Z, Werner D (2011) Fast optimization of electromagnetic design problems using the covariance matrix adaptation evolutionary strategy. IEEE Trans Antennas Propag 59(4):1275–1285

    Article  Google Scholar 

  33. Lagarias J, Reeds J, Wright M, Wright P (1998) Convergence properties of the Nelder-Mead simplex method in low dimensions. Siam J Optim 9:112–147

    Article  MathSciNet  MATH  Google Scholar 

  34. Chakraborty U (2008) Advances in differential evolution. Springer, Heidelberg

    Google Scholar 

  35. Liu B, Wang Y, Yu Z, Liu L, Li M, Wang Z, Lu J, Fernández F (2009) Analog circuit optimization system based on hybrid evolutionary algorithms. Integr VLSI J 42(2):137–148

    Google Scholar 

  36. Deb K (2000) An efficient constraint handling method for genetic algorithms. Comput Methods Appl Mech Eng 186(2):311–338

    Article  MATH  Google Scholar 

  37. Medeiro F, Fernández F, Dominguez-Castro R, Rodriguez-Vazquez A (1994) A statistical optimization-based approach for automated sizing of analog cells. In: Proceedings of the IEEE/ACM international conference on computer-aided design, pp 594–597

    Google Scholar 

  38. Venkatraman S, Yen G (2005) A generic framework for constrained optimization using genetic algorithms. IEEE Trans Evol Comput 9(4):424–435

    Article  Google Scholar 

  39. Deb K, Pratap A, Agarwal S, Meyarivan T (2002) A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evol Comput 6(2):182–197

    Article  Google Scholar 

  40. Zhang Q, Li H (2007) MOEA/D: a multiobjective evolutionary algorithm based on decomposition. IEEE Trans Evol Comput 11(6):712–731

    Article  Google Scholar 

  41. Coello C, Lamont G, Veldhuizen D (2007) Evolutionary algorithms for solving multi-objective problems. Springer, New York

    Google Scholar 

  42. Li H, Zhang Q (2009) Multiobjective optimization problems with complicated Pareto sets, MOEA/D and NSGA-II. IEEE Trans Evol Comput 13(2):284–302

    Article  Google Scholar 

  43. Zielinski K, Laur R (2006) Constrained single-objective optimization using differential evolution. In: Proceedings of IEEE congress on evolutionary computation, pp 223–230

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computing, Glyndwr University, Mold Road, Wrexham, Wales, LL11 2AW, UK

    Bo Liu

  2. Department of Elektrotechniek ESAT-MICAS, Katholieke Universiteit Leuven, Kasteelpark Arenberg 10, B3001, Leuven, Belgium

    Georges Gielen

  3. IMSE-CNM, Universidad de Sevilla and CSIC, Avda. Americo Vespucio s/n, esq. Leonardo da Vinci Isla de l, E-41092, Sevilla, Spain

    Francisco V. Fernández

Authors
  1. Bo Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Georges Gielen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Francisco V. Fernández
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Bo Liu .

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Liu, B., Gielen, G., Fernández, F.V. (2014). Fundamentals of Optimization Techniques in Analog IC Sizing. In: Automated Design of Analog and High-frequency Circuits. Studies in Computational Intelligence, vol 501. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39162-0_2

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-39162-0_2

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-39161-3

  • Online ISBN: 978-3-642-39162-0

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation