A Parametric Programming Approach to Automated Integrated Circuit Design

Stevek, Juraj; Kvasnica, Michal; Fikar, Miroslav; Gomola, Alojz

doi:10.1109/tcst.2017.2716378

Cited by 1 publication

(1 citation statement)

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“…Sparse parameter identification can also be used for predicting the behavioral model of power amplifies as reported in [106] that is performed by using kernel selection and coefficient estimation. The method of parametric optimization in [107] is described for the applications of mobile communication band, low noise amplifier design, and automated integrated circuit design [108]. In the design of integrated circuits, extracting an equivalent model for the used transistor model such as HMET, plays an important role in designing circuits.…”

Section: Equivalent Model Extraction and Parametric Optimizationmentioning

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: Equivalent Model Extraction and Parametric Optimizationmentioning

confidence: 99%