ICTACT Journal on Soft Computing

Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

Meta Heuristic Optimization Approach for CMOS Based Analog Circuit Design and Performance Evaluation of Evolutionary Algorithms


Affiliations
1 Department of Electronics and Communication Engineering, Gujarat Technological University, India
2 Department of Electronics and Communication Engineering, L D College of Engineering, India
     

   Subscribe/Renew Journal


Manual design of Complementary Metal Oxide Semiconductor (CMOS) based analog circuit design becomes more challenging and tedious task due to very complex physical models and variation in the fabrication process as technology scale down. In this continuously changing era, the demand of mixed signal System on Chip (SoC) increasing day by day which digital and analog circuits integrated on same silicon chip. For the digital circuit design, many mature computer based automated tools have been established and limited research efforts made towards automization of the analog circuit design. This gap opens the ample research space for the researcher in the field of analog circuit design. Automization of analog circuit design makes the mixed signal SoC is the best approach to cope up with this problem, cost considerations and the time to market. This motivates the analog circuit designer to explore more automated computer aided tools in the field of analog circuit design. In this review paper, performance evaluation of various evolutionary algorithms is compared. The comparison includes most used Differential Evolution (DE) algorithm, Cuckoo Search (CS) algorithm, Particle Swarm Optimization (PSO) algorithm, hybrid CSPSO algorithm. The performance evaluations of these algorithms are compared with the different standard benchmark functions and the convergence graphs of these standard benchmark functions are compared to test number of runs with respect to number of iterations.

Keywords

Automation of Analog Circuit Design, Optimization, DE Algorithm, PSO Algorithm, Hybrid CSPSO
Subscription Login to verify subscription
User
Notifications
Font Size

  • M.F.M. Barros, J.M.C. Guilherme and N.C.G. Horta, “Analog Circuits and Systems Optimization based on Evolutionary Computation Techniques”, Springer, 2010.

  • S.L. Sabat, K.S. Kumar and S.K. Udgata, “Differential Evolution and Swarm Intelligence Techniques for Analog Circuit Synthesis”, Proceedings of World Congress on Nature and Biologically Inspired Computing, pp. 469-474, 2009.

  • B. Razavi, “Design of Analog CMOS Integrated Circuits”, McGraw-Hill, 2013.

  • G. Alpaydin, S. Balkir and G. Dundar, “An Evolutionary Approach to Automatic Synthesis of High-Performance Analog Integrated Circuits”, IEEE Transactions on Evolutionary Computing, Vol. 7, No. 3, pp. 240-252, 2003.

  • H.Y. Koh, C.H. Sequin and P.R. Gray, “OPASYN: A Compiler for CMOS Operational Amplifiers”, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, Vol. 9, No. 2, pp. 113-125, 1990.

  • P.M.R. Pereira, “Optimization based Design of LC Voltage Controlled Oscillators”, PhD Dissertations, Department of Electronics Engineering, Nova De Lisboa University, pp. 1-198, 2013.

  • B.D. Gajjar, “Automatic CMOS Analog Circuit Design using Particle Swarm Optimization Algorithm”, Master Thesis, Department of Electronics and Communication Engineering, Gujarat University, pp. 1-90, 2011.

  • R.A. Thakker, C. Sathe, A.B. Sachid, M. Shojaei Baghini, V. Ramgopal Rao and M.B. Patil, “A Novel Table-Based Approach for Design of FinFET Circuits”, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, Vol. 28, No. 7, pp. 1061-1070, 2009.

  • Y. Massoud and T. Ragheb, “Automated Design Solutions for Fully Integrated Narrow-Band Low Noise Amplifiers”, Proceedings of International Workshop on System on Chip for Real Time Applications Automated, pp. 109-114, 2006.

  • H.Y. Koh, C.H. Sequin and P.R. Gray, “OPASYN: A Compiler for CMOS Operational Amplifiers”, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, Vol. 9, No. 2, pp. 113-125, 1990.

  • A. Savio, L. Colalongo, M. Quarantelli and Z.M. Kovacs Vajna, “Automatic Scaling Procedures for Analog Design Reuse”, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, Vol. 53, No. 12, pp. 2539-2547, 2006.

  • W. Nye, D.C. Riley, A. Sangiovanni Vincentelli and A.L. Tits, “Delight.Spice: An Optimization-Based System for the Design of Integrated Circuits”, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, Vol. 7, No. 4, pp. 501-519, 2002.

  • S.S. Sapatnekar, V.B. Rao and P.M. Vaidya, “An Exact Solution to the Transistor Sizing Problem for CMOS Circuits using Convex Optimization”, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, Vol. 12, No. 1, pp. 1621-1634, 1993.

  • M.M. Hershenson and S.P. Boyd, “Optimal Design of CMOS Op-Amp via Geometric Programming”, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, Vol. 20, No. 1, pp. 1-21, 2001.

  • F.A. Potra and S.J. Wright, “Interior-Point Methods”, Journal of Computational and Applied Mathematics, Vol. 124, No. 1, pp. 281-302, 2000.

  • R.A. Thakker, M.S. Baghini and M.B. Patil, “Low-Power Low-Voltage Analog Circuit Design using Hierarchical Particle Swarm Optimization”, Proceedings of International Conference on VLSI Design, pp. 427-432, 2009.

  • D.H. Wolpert and W.G. Macready, “No Free Lunch Theorems for Search”, IEEE Transactions on Evolutionary Computing, Vol. 1, No. 1, pp. 67-82, 1997.

  • A.P. Vaze, “Analog Circuit Design using Genetic Algorithm: Modified”, World Academy of Science, Engineering and Technology, International Journal of Electrical, Computer, Energetic, Electronic and Communication Engineering, Vol. 2, No. 2, pp. 301-303, 2008.

  • H. Gupta and B. Ghosh, “Analog Circuits Design using Ant Colony Optimization”, International Journal of Electronics, Computer and Communications Technologies, Vol. 2, No. 3, pp. 9-21, 2012.

  • G. Zhu and S. Kwong, “Gbest-Guided Artificial Bee Colony Algorithm for Numerical Function Optimization”, Applied Mathematics and Computation, Vol. 217, No. 7, pp. 3166-3173, 2010.

  • X.S. Yang and S. Deb, “Cuckoo Search via Levy Flights”, Proceedings of World Congress on Nature and Biologically Inspired Computing, pp. 210–214, 2009.

  • R.A. Vural and U.E. Ayten, “Optimized Analog Filter Approximation Via Evolutionary Algorithms”, Proceedings of International Conference on Intelligent Systems Design and Applications, pp. 485-490, 2012.

  • S.J. Patel and R.A. Thakkar, “Automatic Circuit Design and Optimization using Modified PSO Algorithm”, Journal of Engineering Science and Technology Review, Vol. 4, No. 1, pp. 192-197, 2016.

  • P.P. Prajapati and Mihir V. Shah, “Two Stage CMOS Operational Amplifier Design using Particle Swarm Optimization Algorithm”,Proceeding of IEEE UP Section Conference on Electrical, Computer and Electronics, pp. 1-6, 2015.

  • H. Soneji and R.C. Sanghvi, “Toward the Improvement of Cuckoo Search Algorithm”, World Congress on Information and Communication Technologies, pp. 878-883, 2012.

  • P. Civicioglu and E. Besdok, “A Conceptual Comparison of the Cuckoo-Search, Particle Swarm Optimization, Differential Evolution and Artificial Bee Colony Algorithms”, Artificial Intelligence Review, Vol. 39, No. 4, pp. 315-346, 2013.

  • R. Storn and K. Price, “Differential Evolution - A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces”, Journal of Global Optimization, Vol. 11, No. 4, pp. 341-359, 1997.

  • V. Arunachalam, “Water Resources Research Report: Optimization using Differential Evolution”, Technical Report, University of Western Ontario, pp. 1-68, 2008.

  • J. Kennedy and R.C. Eberhart, “Swarm Intelligence”, Morgan Kaufmann Publishers, 2001.

  • R.C. Butani, B.D. Gajjar and R.A. Thakker, “Performance Evaluation of Particle Swarm Optimization (PSO) and Artificial Bee Colony (ABC) Algorithm”, Proceedings of International Conference on Advanced Computing, Communication and Networks, pp. 108-112, 2011.

  • V. Truong Vu, “A Comparison of Particle Swarm Optimization and Differential Evolution”, Soft Computing, Vol. 3, No. 3, pp. 13-30, 2012.

  • E. Elbeltagi, T. Hegazy and D. Grierson, “Comparison among Five Evolutionary-Based Optimization Algorithms”,

  • Advanced Engineering Informatics, Vol. 19, No. 1, pp. 43-53, 2005.

  • S. Janson and M. Middendorf, “A Hierarchical Particle Swarm Optimizer”, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), Vol. 35, No. 6, pp. 1272-1280, 2005.

  • A. Adnan and M.A. Razzaque, “A Comparative Study of Particle Swarm Optimization and Cuckoo Search Techniques through Problem-Specific Distance Function”, Proceedings of International Conference of Information and Communication Technology, pp. 88-92, 2013.

  • X.S. Yang, “Harmony Search as a Metaheuristic Algorithm”, Computational Intelligence, Vol. 191, pp. 1-14, 2009.

  • E. Valian, S. Mohanna and S. Tavakoli, “Improved Cuckoo search Algorithm for Global Optimization”, Communication and Information Technology, Vol. 1, No. 1, pp. 31-44, 2011.

  • Pankaj P. Prajapati, Swati A. Sharma and Mihir V. Shah, “Design of CMOS Operational Amplifier using Differential Evolutionary Algorithm”, Proceedings of International Conference on VLSI Design, pp. 108-111, 2016.

  • I. Fister, D. Fister and I. Fister, “A Comprehensive Review of Cuckoo Search: Variants and Hybrids”, International Journal of Mathematical Modelling and Numerical Optimisation, Vol. 4, No. 4, pp. 387-409, 2013.

  • S. Roy, “Cuckoo Search Algorithm using Levy Flight : A Review”, International Journal of Modern Education and Computer Science, Vol. 5, No. 12, pp. 10-15, 2013.

  • X. Yang, T. O. Ting and M. Karamanoglu, “Random Walks, Levy Flights, Markov Chains and Metaheuristic Optimization”, Springer, 2013.

  • X. Yang and S. Deb, “Cuckoo Search : State-of-the-Art and Opportunities”, Proceedings of International Conference on Soft Computing and Machine Intelligence, pp. 55-59, 2017.

  • R.N. Mantegna, “Fast, Accurate Algorithm for Numerical Simulation of Levy Stable Stochastic Processes”, Physical Review, Vol. 49, No. 5, pp. 4677-4683, 1994.

  • Pankaj P. Prajapati and Mihir V. Shah, “Optimization of CMOS Current Mirror Load-Based Differential Amplifier using Hybrid Cuckoo Search and Particle Swarm Optimization Algorithm”, Journal of Artificial Intelligence Research and Advances, Vol. 5, No. 3, pp. 71-78, 2019.

  • M.A. Mushahhid Majeed and S.R. Patri, “A Hybrid of WOA and mGWO Algorithms for Global Optimization and Analog Circuit Design Automation”, International Journal for Computation and Mathematics in Electrical and Electronic Engineering, Vol. 38, No. 1, pp. 452-476, 2018.

  • F.J. Rodriguez, C. Garcia Martinez and M. Lozano, “Hybrid Metaheuristics based on Evolutionary Algorithms and Simulated Annealing : Taxonomy, Comparison, and Synergy Test”, IEEE Transactions on Evolutionary Computing, Vol. 16, No. 6, pp. 787-800, 2012.

  • A. Ghodrati and S. Lotfi, “A Hybrid CS/PSO Algorithm for Global Optimization”, Proceedings of International Conference on Intelligent Information and Database Systems, pp. 89-98, 2012.

  • X. Yang, “Recent Advances in Swarm Intelligence and Evolutionary Computation”, Springer, 2015.

  • P.P. Prajapati and M.V. Shah, “Performance Estimation of Differential Evolution, Particle Swarm Optimization and Cuckoo Search Algorithms”, International Journal on Intelligent Systems and Applications, Vol. 6, pp. 59-67, 2018.

  • M. Molga and C. Smutnicki, “Test Functions for Optimization Needs”, Proceedings of International Conference on Computer and Information Science, pp. 1-43, 2005.

  • M. Jamil and X.S. Yang, “A Literature Survey of Benchmark Functions for Global Optimization Problems”, International Journal of Mathematical Modelling and Numerical Optimisation, Vol. 4, No. 2, pp. 150-194, 2013.

  • P.P. Prajapati and M.V. Shah, “Computer Aided CMOS Based Analog Circuit Design”, Ph.D. Dissertations, Department of Electrical and Electronics Engineering, Gujarat Technological University, pp. 1-123, 2019.

  • M.M. Ali and A. Torn, “Population Set-Based Global Optimization Algorithms: Some Modifications and Numerical Studies”, Computers and Operations Research, Vol. 31, No. 10, pp. 1703-1725, 2004.

  • Pankaj P. Prajapati and Mihir V. Shah, “Automatic Circuit Design of CMOS Miller OTA using Cuckoo Search Algorithm”, International Journal of Applied Metaheuristic Computing, Vol. 23, No. 1, pp. 1-13, 2018.

  • M. Clerc, “The Swarm and the Queen: Towards a Deterministic and Adaptive Particle Swarm Optimization”, Proceedings of Congress on Evolutionary Computation, Vol. 3, pp. 1951-1957, 1999.

  • I.C. Trelea, “The Particle Swarm Optimization Algorithm : Convergence Analysis and Parameter Selection”, Information Processing Letters, Vol. 85, No. 6, pp. 317-325, 2003.

  • Y. Shi and R.C. Eberhart, “Parameter Selection in Particle Swarm Optimization”, Springer, 1998.

  • C. Eberhart and Y. Shi, “Comparing Inertia Weights and Constriction Factors in Particle Swarm Optimization”, Proceedings of the Congress on Evolutionary Computation, pp. 84-88, 2000.

  • Pankaj P. Prajapati and Mihir V. Shah, “Automatic Sizing of CMOS based Analog Circuits using Cuckoo Search Algorithm”, International Journal of Intelligent Systems Technologies and Applications, Vol. 23, No. 1, pp. 1-14, 2018.

  • A. Leon-Garcia, “Probability, Statistics, and Random Processes for Electrical Engineering”, Pearson Prentice Hall, 2008.


Abstract Views: 93

PDF Views: 2




  • Meta Heuristic Optimization Approach for CMOS Based Analog Circuit Design and Performance Evaluation of Evolutionary Algorithms

Abstract Views: 93  |  PDF Views: 2

Authors

Sureshbhai L. Bharvad
Department of Electronics and Communication Engineering, Gujarat Technological University, India
Pankaj P. Prajapati
Department of Electronics and Communication Engineering, L D College of Engineering, India
Anilkumar J. Kshatriya
Department of Electronics and Communication Engineering, L D College of Engineering, India

Abstract


Manual design of Complementary Metal Oxide Semiconductor (CMOS) based analog circuit design becomes more challenging and tedious task due to very complex physical models and variation in the fabrication process as technology scale down. In this continuously changing era, the demand of mixed signal System on Chip (SoC) increasing day by day which digital and analog circuits integrated on same silicon chip. For the digital circuit design, many mature computer based automated tools have been established and limited research efforts made towards automization of the analog circuit design. This gap opens the ample research space for the researcher in the field of analog circuit design. Automization of analog circuit design makes the mixed signal SoC is the best approach to cope up with this problem, cost considerations and the time to market. This motivates the analog circuit designer to explore more automated computer aided tools in the field of analog circuit design. In this review paper, performance evaluation of various evolutionary algorithms is compared. The comparison includes most used Differential Evolution (DE) algorithm, Cuckoo Search (CS) algorithm, Particle Swarm Optimization (PSO) algorithm, hybrid CSPSO algorithm. The performance evaluations of these algorithms are compared with the different standard benchmark functions and the convergence graphs of these standard benchmark functions are compared to test number of runs with respect to number of iterations.

Keywords


Automation of Analog Circuit Design, Optimization, DE Algorithm, PSO Algorithm, Hybrid CSPSO

References