Generation of surrogate models of Pareto-optimal performance trade-offs of planar inductors

Kotti, Mouna; Gonzalez-Echevarria, R.; Fernández, Francisco V.; Roca, E.; Sieiro, J.; Castro-López, R.; Fakhfakh, Mourad; López-Villegas, J.M.

doi:10.1007/s10470-013-0230-8

Cited by 17 publications

(8 citation statements)

References 19 publications

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“…The main objective of the paper is to present a fast way to evaluate LNA performances, particularly IIP3. The proposed approach can be extended to use complex models of integrated inductors such as those proposed in [32], and also sensitivity and yield analysis.…”

Section: Discussionmentioning

confidence: 99%

“…Actually, it offers a very interesting advantage since, in many engineering applications, particularly in analogue circuit design, such evaluations/measures can be very time consuming, mainly when using an in-loop technique. For example, in [32] the sizing of integrated inductors is considered, and it is shown that, due to the electromagnetic simulations, it takes around two weeks to generate the Pareto front of three performances (Inductance/Area/Quality factor) of a square integrated inductor.…”

Section: Overview Of Surrogate Modelsmentioning

confidence: 99%

“…It is to be mentioned that, when compared to the other modelling techniques, the kriging approach offers several advantages, such as the simplicity of its implementation, its accuracy, and the fact that it is able to provide an estimation of the model's error [32,36]. The kriging technique using a Gaussian correlation function is adopted in this work.…”

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confidence: 99%

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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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Section: Discussionmentioning

confidence: 99%

Section: Overview Of Surrogate Modelsmentioning

confidence: 99%

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Metamodelling Techniques for the Optimal Design of Low-Noise Amplifiers

et al. 2020

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“…Here, we use Kriging (also called the Gaussian process) as a surrogate. Kriging has been widely applied in deterministic simulation models in a plethora of engineering applications, such as automotive, 63 aeronautics, 64 and electronics 65,66 to name a few; however, it is hardly used in the stochastic (random) simulation models, see References 67‐69.…”

Section: Surrogate‐based Robust Simulation‐optimization (Two‐layer Sumentioning

confidence: 99%

Robust tuning and sensitivity analysis of stochastic integer andfractional‐order PIDcontrol systems: application ofsurrogate‐basedrobustsimulation‐optimization

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Fakhfakh

Kotti

et al. 2020

Int J Numerical Modelling

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This paper aims to make a trade‐off between performance and robustness in stochastic control systems with probabilistic uncertainties. For this purpose, we develop a surrogate‐based robust simulation‐optimization approach for robust tuning and analyzing the sensitivity of stochastic controllers. Kriging surrogate is combined with robust design optimization to construct a robust simulation‐optimization model in the class of dual response surfaces. Randomness in simulation experiments due to uncertainty is analyzed through bootstrapping technique by computing confidence regions for the estimation of Pareto frontier. Results confirmed a proper trade‐off between the model's performance with the measure of expected Integral Squared Error (ISE) and robustness against uncertainty in the plant's physical parameters. Finally, the proposed method is evaluated in terms of accuracy, computational cost, and simplicity particularly in comparison with some common existed techniques in the tuning of the Proportional‐Integral‐Derivative (PID) and Fractional‐Order PID (FOPID) controllers.

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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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Generation of surrogate models of Pareto-optimal performance trade-offs of planar inductors

Cited by 17 publications

References 19 publications

Metamodelling Techniques for the Optimal Design of Low-Noise Amplifiers

Metamodelling Techniques for the Optimal Design of Low-Noise Amplifiers

Robust tuning and sensitivity analysis of stochastic integer andfractional‐order PIDcontrol systems: application ofsurrogate‐basedrobustsimulation‐optimization

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

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