Abstract
This work presents an optimal design of low noise amplifier (LNA) using Firefly Algorithm (FA). In this work we have implemented FA in optimizing various parameters of LNA like linearity, Gain, Noise Figure (NF), input and output matching simultaneously satisfying all the constraint. Since there are five objectives to optimize, they can treated as multiobjective optimization. Weighted sum approach is used to convert these objectives into single objective function. Weights are given according to the priority of objective function. The designed LNA has a cascode structure with inductive source degeneration topology and is implemented and simulated in UMC 0.18 μm CMOS technology using CADENCE tool. The designed LNA has a simulated values: IIP3 of − 2.60 dBm, a gain of 22.15 dB and NF of 1.168 dB at 5.5 GHz frequency. The optimized value of LNA parameter using FA when simulated on MATLAB environment is found to be 0.356 dBm, 23.01, 1.24, − 26.56 and − 18.18 dB for IIP3, Gain, Noise Figure, input and output reflection coefficient, respectively. Results obtained from FA are also compared with Particle Swarm Optimization (PSO) and Cuckoo Search Algorithm (CSA). Results depicts the better performance of FA over PSO and CSA.
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References
Aarts E, Lenstra K (2003) Local search in combinatorial optimization. Princeton University Press, Princeton
Deb K (2004) Single and multi-objective optimization using evolutionary algorithms. In: Presented at KanGAL Report No. 2004002, February
Dorigo M, Maniezzo V, Colorni A (1996) The ant system: optimization by a colony of cooperating agents. IEEE Trans Syst Man Cybernet Part B 26:29–41
Ellinger F (2008) Radio frequency integrated circuits and technologies, 2nd edn. Springer, New York
Fakhfakh M, Cooren Y, Sallem A, Loulou M, Siarry P (2010) Analog circuit design optimization through the particle swarm optimization technique. Analog Integr Circuits Signal Process 63:71–82
Habib R, Ahmad H (2013) A high linearity CMOS low noise amplifier for 3.66 GHz applications using current-reused topology. Microelectron J 44(4):301–306
Jones DR, Perttunen CD, Stuckman BE (1993) Lipschitzian optimization without the Lipschitz constant. J Optim Theory Appl 79:157–181
Kaveh A, Talatahari S (2010) A novel heuristic optimization method: charged system search. Acta Mech 213:267–289
Kaveh A, Mohammad A, Share M, Moslehi M (2013) Magnetic charged system search: a new meta-heuristic algorithm for optimization. Acta Mech 224:85–107
Kennedy J, Eberhart RC (1995) Particle swarm optimization. Proc IEEE Int Conf Neural Netw 4:1942–1948
Khong SZ, Nesic D, Manzie C, Tan Y (2013) Multidimensional global extremum seeking via the DIRECT optimisation algorithm. Automatica 49:1970–1978
Kirkpatric S, Gelatt CD, Vecchi MP (1983) Optimization by simulated annealing. J Sci 220:671–680
Kotti M, Sallem A, Bougharriou M, Fakhfakh M, Loulou M (2010) Optimizing CMOS LNA circuits through multi-objective meta heuristics. In: XIth international workshop on symbolic and numerical methods modeling and applications to circuit design
Kumar R, Rajan A, Talukdar FA, Dey N, Santhi V, Balas VE (2016a) Optimization of 5.5-GHz CMOS LNA parameters using firefly algorithm. Neural Comput Appl 28:1–15
Kumar R, Devi A, Sarkar A, Talukdar FA (2016b) Design of 5.5 GHz linear low noise amplifier using post distortion technique with body biasing. Microsyst Technol 22(11):2681–2690
Lee TH (2002) 5-GHz CMOS wireless LANs. IEEE Trans Microw Theory Tech 50:268–280
Li Y (2009) A simulation-based evolutionary approach to LNA circuit design optimization. Appl Math Comput 209:57–67
Mohsen H, Sajad Ch, Sepehr Z (2018) Design of UWB low noise amplifier using noise-canceling and current-reused techniques. Integr VLSI J 60:232–239
Rajan A, Malakar T (2015) Optimal reactive power dispatch using hybrid Nelder–Mead simplex based firefly algorithm. Int J Electr Power Energy Syst 66:9–24
Rashedi E, Nezamabadi-pour H, Saryazdi S (2009) GSA: a gravitational search algorithm. Inform Sci 179:2232–2248
Razavi B (1997) RF microelectronics. Prentice-Hall PTR, Upper Saddle River
Roya J, Abumoslem J, Jafar S (2017) Pre-distortion technique to improve linearity of low noise amplifier. Microelectron J 61:95–105
Shams M, Rashedi E, Hakimi A (2015) Clustered-gravitational search algorithm and its application in parameter optimization of a low noise amplifier. Appl Math Comput 258:436–453
Taherzadeh M, Lotfi R, Zare H, Shoaei O (2003) Design optimization of analog integrated circuits using simulation-based genetic algorithm. Proc IEEE Int Symp Signals Circuits 1:73–76
Xiaohua F, Heng Z, Edgar S (2008) A noise reduction and linearity improvement technique for a differential Cascode LNA. IEEE J Solid State Circuits 43(3):588–599
Yang XS (2008) Nature-inspired metaheuristic algorithms. Firefly Algorithm 20:79–90
Yang X, Deb S (2009) Cuckoo search via levy flights. In: World congress on nature and biologically inspired computing. IEEE
Zitzler E (1999) Evolutionary algorithms for multi-objective optimization: methods and applications. PhD thesis, Swiss Federal Institute of Technology, Zurich, Switzerland
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Kumar, R., Talukdar, F.A., Rajan, A. et al. Parameter optimization of 5.5 GHz low noise amplifier using multi-objective Firefly Algorithm. Microsyst Technol 26, 3289–3297 (2020). https://doi.org/10.1007/s00542-018-4034-8
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DOI: https://doi.org/10.1007/s00542-018-4034-8