Optimizing CMOS LNA circuits through multi-objective meta heuristics

Kotti, Mouna; Sallem, Amin; Bougharriou, Mariam; Fakhfakh, Mourad; Loulou, Mourad

doi:10.1109/sm2acd.2010.5672305

Cited by 14 publications

(6 citation statements)

References 17 publications

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“…In RF receivers, LNAs are arguably the most important circuits because their performances can considerably alter those of the rest of the system [2,[38][39][40].…”

Section: On the Modelling Of Low-noise Amplifiers' (Lnas) Performancesmentioning

confidence: 99%

Metamodelling Techniques for the Optimal Design of Low-Noise Amplifiers

et al. 2020

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In this article we deal with the optimal sizing of low-noise amplifiers (LNAs) using newly proposed metamodeling techniques. The main objective is to construct metamodels of main performances of the LNAs (namely, the third intercept point (IIP3), the scattering parameters (Sij), and the noise figure (NF)) and use them inside an optimization kernel for maximizing the circuits’ performances. The kriging surrogate modelling technique is used for constructing these models. The particle swarm optimization (PSO) technique is considered as the optimization metaheuristic. Two CMOS amplifiers are considered: a UMTS LNA and a multistandard LNA. Obtained results show that, at the considered working frequencies, the first LNA exhibits at 2.14 GHz a noise figure of 1.30 dB, an S21 of 16.01 dB, an S11 of −12.60 dB, and an IIP3 of 8.30 dBm. At 2 GHz, the second LNA has a noise figure of 1.24 dB, an S21 of 17.16 dB, an S11 of −13.74 dB, and an IIP3 of 4.30 dBm. Comparisons between results obtained using the constructed models and those of the simulation are presented to show the perfect agreement between them.

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“…In RF receivers, LNAs are arguably the most important circuits because their performances can considerably alter those of the rest of the system [2,[38][39][40].…”

Section: On the Modelling Of Low-noise Amplifiers' (Lnas) Performancesmentioning

confidence: 99%

Metamodelling Techniques for the Optimal Design of Low-Noise Amplifiers

et al. 2020

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“…Heuristic search algorithm Simulator [15] GA, SA, LM 2 ADS [16] IGA a ADS [17] Layered encoding structure using combination of GA and PSO ADS [18] GA ADS [19] MOPSO-CD b and NSGA-II c ADS [20] PSO _ [21] Combination of GA and multi objective PSO (programmed in C++ software) ADS [14] GA, LM Hspice [22] Modified genetic algorithm and PSO Hspice RF [23] Genetic algorithm and Multi-Objective PSO Hspice RF [24] Multi objective genetic algorithm Hspice RF [25] NSGA-II Hspice RF [26] Pareto-based multi-objective genetic algorithm Hspice RF a Interactive genetic algorithm. b Multi-Objective Optimization algorithm PSO incorporating the mechanism of the crowding distance technique.…”

Section: Articlementioning

confidence: 99%

“…To optimize LNA, Ref. [19] used a Multi-Objective Optimization algorithm PSO incorporating the mechanism of the crowding distance technique (MOPSO-CD) and ADS as its simulator.…”

Section: Articlementioning

confidence: 99%

Clustered-gravitational search algorithm and its application in parameter optimization of a low noise amplifier

Shams

Rashedi

Hakimi

2015

Applied Mathematics and Computation

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“…Fortunately, multi-objective metaheuristics offer excellent solutions for handling such further complex optimization problems. They have already been used for optimally sizing analog, mixed signal and radio-frequency circuits; see for instance [1,2,4,[10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Fast and Efficient Sensitivity Aware Multi-Objective Optimization of Analog Circuits

et al. 2019

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This article introduces a novel approach for generating low-sensitive Pareto fronts of analog circuit performances. The main idea consists of taking advantage from the social interaction between particles within a multi-objective particle swarm optimization algorithm by progressively guiding the global leading process towards low sensitive solutions inside the landscape. We show that the proposed approach significantly outperforms already proposed techniques dealing with the generation of sensitivity-aware Pareto fronts, not only in terms of computing time, but also with regards to the number of solutions forming the tradeoff surface. Performances of our approach are highlighted via the design of two analog circuits.

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Optimizing CMOS LNA circuits through multi-objective meta heuristics

Cited by 14 publications

References 17 publications

Metamodelling Techniques for the Optimal Design of Low-Noise Amplifiers

Metamodelling Techniques for the Optimal Design of Low-Noise Amplifiers

Clustered-gravitational search algorithm and its application in parameter optimization of a low noise amplifier

Fast and Efficient Sensitivity Aware Multi-Objective Optimization of Analog Circuits

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