Artificial neural network approach for LNA design of GPS receiver

Singh, S. P.; Chopra, P. K.

doi:10.3103/s1060992x16040111

Cited by 4 publications

(2 citation statements)

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“…Neural networks are very effective tools used in various design steps of analog and RF circuits [43][44][45]. They are as a 'black-box' modeling and are exerted in modeling of onchip inductors [46], semiconductor devices [47], conventional analog circuit building blocks [48,49], analog IC sizing [6,50], and in crucial RF circuit blocks such as power amplifiers [51], RF front-end receivers [52], low noise amplifiers [53], voltage-controlled oscillators [54,55], and multiple-input multiple-output (MIMO) systems [56,57].…”

Section: Neural Network Techniquementioning

confidence: 99%

Optimization techniques for analog and RF circuit designs: an overview

Kouhalvandi

Ceylan²,

Özoğuz

2020

Analog Integr Circ Sig Process

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Optimizations have gained much consideration from the researchers working in the domains of analog and radio frequency (RF), recently. Dealing with highly nonlinear behavior of active components and aiming to meet design specifications are common issues in all nonlinear circuit designs. As a consequence and accordingly, many studies have been conducted on diverse optimization methods and algorithms for tackling the design problems and meeting optimal solutions with high accuracy. The main purpose of this article is to provide a comprehensive and systematic literature review for the optimization approaches applied by the researchers for designing various analog and microwave circuits. We focus on considering the existing optimization methods from the newly published optimization methods in the last decade. Thus, this study can guide and enlighten complementary metal-oxide-semiconductor analog and RF microwave circuit designers to consider optimization methods commonly used in both areas and to expand conventional performance figures used in their area.

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Section: Neural Network Techniquementioning

confidence: 99%

Optimization techniques for analog and RF circuit designs: an overview

Kouhalvandi

Ceylan²,

Özoğuz

2020

Analog Integr Circ Sig Process

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“…It is worth noting that GA required a relatively high number of generations. In [43], the modeling of an LNA was proposed using the Levenberg-Marquardt (LM) algorithm with a limited set of circuit parameters, such as frequency (f), drain-to-source voltage (Vds), drain-to-source current (Ids), and temperature (T). However, matching networks were not part of the model.…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Network Modeling of a CMOS Differential Low-Noise Amplifier Using the Bayesian Regularization Algorithm

Subburaman,

Thangaraj,

Balu

et al. 2023

Sensors

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The purpose of this communication is to present the modeling of an Artificial Neural Network (ANN) for a differential Complementary Metal Oxide Semiconductor (CMOS) Low-Noise Amplifier (LNA) designed for wireless applications. For satellite transponder applications employing differential LNAs, various techniques, such as gain boosting, linearity improvement, and body bias, have been individually documented in the literature. The proposed LNA combines all three of these techniques differentially, aiming to achieve a high gain, a low noise figure, excellent linearity, and reduced power consumption. Under simulation conditions at 5 GHz using Cadence, the proposed LNA demonstrates a high gain (S21) of 29.5 dB and a low noise figure (NF) of 1.2 dB, with a reduced supply voltage of only 0.9 V. Additionally, it exhibits a reflection coefficient (S11) of less than −10 dB, a power dissipation (Pdc) of 19.3 mW, and a third-order input intercept point (IIP3) of 0.2 dBm. The performance results of the proposed LNA, combining all three techniques, outperform those of LNAs employing only two of the above techniques. The proposed LNA is modeled using PatternNet BR, and the simulation results closely align with the results of the developed ANN. In comparison to the Cadence simulation method, the proposed approach also offers accurate circuit solutions.

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