Variability aware transistor stack based regression surrogate models for accurate and efficient statistical leakage estimation

Garg, Lokesh

doi:10.1016/j.mejo.2017.05.015

Cited by 2 publications

(2 citation statements)

References 23 publications

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“…In [33], the response surface methodology is used within the multi-objective optimization in the aim to implement an accurate solution for the optimization of RF-MEMS switches. In [34], the authors used the support vector machine for standby statistical leakage estimation of CMOS circuits using sampling based methods, which your aim is to replace SPICE simulation with your proposed surrogate models.…”

Section: Overview Of Surrogate Modelsmentioning

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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Section: Overview Of Surrogate Modelsmentioning

confidence: 99%

Metamodelling Techniques for the Optimal Design of Low-Noise Amplifiers

et al. 2020

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“…Various optimization techniques are used to find optimal designs and improve the device performance at the early design stage [1][2][3]. While in other engineering domains it is sometimes possible to solve complex numerical equations with efficient-stable numerical methods [4][5][6]; given the high computational cost of simulating modern high-frequency circuits, surrogate modeling techniques are typically utilized to efficiently perform design optimization [7][8][9][10][11]. In particular, since the complexity of design optimization problems is constantly increasing, machine learning-based algorithms have become a popular choice to cope with the multiscale issues in radio frequency (RF) and microwave designs [12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

A Robust Bayesian Optimization Framework for Microwave Circuit Design under Uncertainty

et al. 2022

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In modern electronics, there are many inevitable uncertainties and variations of design parameters that have a profound effect on the performance of a device. These are, among others, induced by manufacturing tolerances, assembling inaccuracies, material diversities, machining errors, etc. This prompts wide interests in enhanced optimization algorithms that take the effect of these uncertainty sources into account and that are able to find robust designs, i.e., designs that are insensitive to the uncertainties early in the design cycle. In this work, a novel machine learning-based optimization framework that accounts for uncertainty of the design parameters is presented. This is achieved by using a modified version of the expected improvement criterion. Moreover, a data-efficient Bayesian Optimization framework is leveraged to limit the number of simulations required to find a robust design solution. Two suitable application examples validate that the robustness is significantly improved compared to standard design methods.

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Variability aware transistor stack based regression surrogate models for accurate and efficient statistical leakage estimation

Cited by 2 publications

References 23 publications

Metamodelling Techniques for the Optimal Design of Low-Noise Amplifiers

Metamodelling Techniques for the Optimal Design of Low-Noise Amplifiers

A Robust Bayesian Optimization Framework for Microwave Circuit Design under Uncertainty

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