Skip to main content
Log in

Optimal design of low power high gain and high speed CMOS circuits using fish swarm optimization algorithm

  • Original Article
  • Published:
International Journal of Machine Learning and Cybernetics Aims and scope Submit manuscript

Abstract

This work presents a swarm intelligent optimization methodology based on swarming principle and collective behaviour of natural species termed as fish swarm optimization algorithm (FSOA). It is applied for the optimization for the design of a Complementary Metal Oxide semiconductor (CMOS) two-stage comparator and a folded cascode operational trans-conductance (FCOTA). The basic idea of FSOA is to model the traditional behavior of fish such as preying, swarming followed by local fish of individual optimization for global search. The suitably chosen control parameters of FSOA balance the exploration and exploitation of search space. Results obtained by SPICE for the two analog circuits, justify the capability of producing a desired result and the superiority of FSOA over other evolutionary optimization algorithms like HS (harmonic search), PSO (particle swarm optimization) and DE (differential evolution) in terms of convergence speed and design performances. The results obtained using FSOA show improved performances for the designed analog circuits. The circuits designed using FSOA take lesser areas for MOS transistors; both dissipate low powers and provide high gains. The performances of FSOA based designed circuits are significantly better than those of reported works. Simulation results obtained from SPICE also prove that FSOA is the best in comparison with the previously reported techniques in terms of the area occupied by CMOS transistors, gain, power etc. Cadence version 5.10.41 is used to perform the simulation using TSMC 0.35μm and TSMC 1.25μm technology parameters for two-stage comparator and FCOTA, respectively.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22
Fig. 23
Fig. 24
Fig. 25
Fig. 26
Fig. 27
Fig. 28
Fig. 29
Fig. 30

Similar content being viewed by others

References

  1. Mallick S, Kar R, Mandal D, Ghoshal SP (2015) CMOS analogue amplifier circuits optimization using hybrid backtracking search algorithm with differential evolution. Journal of Experimental & Theoretical Artificial -Intelligence 28(4):719–749

    Article  Google Scholar 

  2. Kennedy J, Eberhart R (1995) Particle swarm optimization. Proc IEEE Int Conf Neural Netw 4:1942–1948

    Article  Google Scholar 

  3. Vural RA, Yildirim T (2012) Analog circuit sizing via swarm intelligence. AEU Int J Electr Commun 66(9):732–740

    Article  Google Scholar 

  4. Vural RA, Yildirim T (2011) Swarm intelligence based sizing methodology for CMOS operational amplifier. In: Proc. 12th IEEE Symp. on Computational Intelligence and Informatics, pp 525–528

  5. Ceperic V, Butkovic Z, Baric A (2006) Design and optimization of self-biased complementary folded cascade. In: Proc. IEEE mediterranean electrotechnical conference (MELECON), pp 145–148

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

    Article  Google Scholar 

  7. Hershenson M, Boyd SP, Lee TH (2001) Optimal design of a CMOS op-amp via geometric programming. IEEE Trans Comput Aided Des Integr Circuits Syst 20:1–21

    Article  Google Scholar 

  8. Ho CS, Liou JJ (1995) VLSI neural network circuit using a multiple-input trans-conductance amplifier and digital multiplexers. Int J Electron 79:577–583

    Article  Google Scholar 

  9. Liu BD, Lee JY, Wang HH (1987) Parameter extraction and optimization for MOSFET models. Int J Electron 63:873–884

    Article  Google Scholar 

  10. Chen YL, Wu WR, Liu CNJ, Li JCM (2014) Simultaneous optimization of analog circuits with reliability and variability for applications on flexible electronics. IEEE Trans Comput Aided Des Integr Circuits Syst 33:24–35

    Article  Google Scholar 

  11. De BP, Kar R, Mandal D, Ghoshal SP (2016) An efficient design of CMOS comparator and folded cascode op-amp circuits using particle swarm optimization with an aging leader and challengers algorithm. Int J Mach Learn Cybernet 7(2):325–344

    Article  Google Scholar 

  12. Taormina R, Chau KW (2015) ANN-based interval forecasting of stream flow discharges using the LUBE method and MOFIPS. Eng Appl Artif Intell 45(C):429–440

    Article  Google Scholar 

  13. Zhang J, Chau KW (2009) Multilayer ensemble pruning via novel multi-sub-swarm particle swarm optimization. J Univ Comput Sci 15(4):840–858

    Google Scholar 

  14. Wang WC, Chau KW, Xiu DM, Chen XY (2015) Improving forecasting accuracy of annual runoff time series using ARIMA based on EEMD decomposition. Water Resour Manag 29(8):2655–2675

    Article  Google Scholar 

  15. Zhang SW, Chau KW (2009) Dimension reduction using semi-supervised locally linear embedding for plant leaf classification. Lect Notes Comput Sci LNCS 5754:948–955

    Article  Google Scholar 

  16. Wu CL, Chau KW, Li YS (2009) Methods to improve neural network performance in daily flows prediction. J Hydrol 372(1–4):80–93

    Article  Google Scholar 

  17. Chau KW, Wu CL (2010) A hybrid model coupled with singular spectrum analysis for daily rainfall prediction. J Hydroinformatics 12(4):458–473

    Article  Google Scholar 

  18. Vural RA, Bozkurt U, Yildirim T (2013) Metaheuristics based CMOS two-stage comparator optimization. In: Proceedings of the world congress on engineering and computer science 2013, vol. II, WCECS 2013, 23–25 October, 2013, San Francisco, USA

  19. Rajni E (2011) Design of high gain folded-cascode operational amplifier using 1.25 μm CMOS technology. Int J Sci Eng Res 2(11). ISSN 2229-5518

  20. Mohan P (2010) Sensitivity analysis of third and fourth-order filters. Circuits Syst Signal Process 29(5):999–1005

    Article  MATH  Google Scholar 

  21. Gomez G, Cuautle ET, de la Fraga LG (2013) Richardson extrapolation-based sensitivity analysis in the multi-objective optimization of analog circuits. Appl Math Comput 222:167–176

    MATH  Google Scholar 

  22. Azizi R (2014) Empirical study of artificial fish swarm algorithm. Int J Comput Commun Netw 3(1):1–7. ISSN 2319-2720

  23. Neshat M, Adeli A, Sepidnam G, Sargolzaei M, Toosi AN (2012) A review of artificial fish swarm optimization methods and applications. Int J Smart Sens Intell Syst 5(1):107

  24. Neshat M, Sepidnam G, Sargolzaei M, Toosi AN (2014) Artificial fish swarm algorithm: a survey of the state-of-the-art, hybridization, combinatorial and indicative applications. Artif Intell Rev 42(4):965–997

    Article  Google Scholar 

  25. Luo Y, Zhang J, Li X (2007) The optimization of PID controller parameters based on artificial fish swarm algorithm. In: Proceedings of the IEEE international conference on automation and logistics, August 18–21, 2007, Jinan, China

  26. Lobato FS, Steffen V Jr (2014) Fish swarm optimization algorithm applied to engineering system design. Latin Am J Solids Struct 11:143–156

    Article  Google Scholar 

  27. Allen P, Holberg D (2002) CMOS analog circuit design, 2nd edn. Oxford University Press, New York

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Kanchan Baran Maji.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Maji, K.B., Kar, R., Mandal, D. et al. Optimal design of low power high gain and high speed CMOS circuits using fish swarm optimization algorithm. Int. J. Mach. Learn. & Cyber. 9, 771–786 (2018). https://doi.org/10.1007/s13042-016-0606-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13042-016-0606-z

Keywords

Navigation