An Evolutionary Algorithm-Based Approach to Robust Analog Circuit Design using Constrained Multi-Objective Optimization

Nicosia, Giuseppe; Rinaudo, Salvatore; Sciacca, Eva

doi:10.1007/978-1-84800-094-0_2

Cited by 13 publications

(13 citation statements)

References 10 publications

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“…Compute the output estimation: make use of (25) or (26) and compute the estimation vector pðy est i Þ, employ (38) to normalize the vector pðy est i Þ, if the plausibility of the results is acceptable i.e. Pðy i Þ P Pðy i Þ min , then continue with the state estimation process, else do measurements.…”

Section: Modelling Algorithmmentioning

confidence: 99%

“…Increasing the quality of the models has been one of the favourite research directions during the last years. This involves the exponential growth of non-conventional modelling based on knowledge-based systems (KBS) tools including fuzzy logic [5,26,29,46,50,55], neural networks [1,33,51,52], genetic algorithms [14,24,38,48], data mining techniques [8,9,36,56] etc., and their merge resulting in hybrid models [2,6,7,25,30,39].…”

Section: Introductionmentioning

confidence: 99%

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New results in modelling derived from Bayesian filtering

Pozna

Precup

Tar

et al. 2010

Knowledge-Based Systems

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Section: Modelling Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

New results in modelling derived from Bayesian filtering

Pozna

Precup

Tar

et al. 2010

Knowledge-Based Systems

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“…Only devices with high performance and short developingperiod can win the market [13]. This research work shows Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page.…”

Section: Introductionmentioning

confidence: 97%

Multi-objective optimization of doping profile in semiconductor design

Stracquadanio

Drago

Romano

et al. 2010

Proceedings of the 12th Annual Conference on Genetic and Evolutionary Computation

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A crucial task in designing semiconductor devices is to provide a doping profile that assures specific electrical properties. This research work is focused in redesigning doping profiles of semiconductor devices in order to obtain an increased output current; however, large doping levels can degenerate the devices and hence a trade-off between doping profile deviation and output current should be found. The doping profile optimization in semiconductor has been tackled as a multi-objective optimization problem using the Nondominated Sorting Genetic Algorithm (NSGA-II). We focus on silicon diodes and MOSFET devices; firstly, we redesign the doping profile of diodes in order to obtain a trade-off between doping profile deviation and output current. Secondly, we find a trade-off between current and temperature for a MOSFET device. The experimental results confirm the effectiveness of the proposed approach to face this class of problems in electronic design automation.

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“…Design automation targets the reduction of designer effort, minimize time-to market and consequently minimize production costs [3]. Presently, digital synthesis is mostly automatic and is mainly enabled by intellectual property (IP) reuse.…”

Section: Introductionmentioning

confidence: 99%

An evolutionary approach for Nominal design and yield enhancement of analog amplifiers

Farago

Oltean

Faragó

et al. 2015

2015 IEEE 10th Jubilee International Symposium on Applied Computational Intelligence and Informatics

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The microelectronic industry is driven by the continuous demand for processing speed and capacity. To answer such demands, novel design paradigms target design automation. While digital design is mostly automatic, design automation in the analog domain is limited and mainly used for high-level synthesis. A promising solution to overcome limitations and constrains of automatic analog design is to involve computational intelligence techniques. This work proposes an evolutionary design methodology to bring contributions to transistor-level design automation. The proposed framework is built around a typical design automation workflow: Feasibility design, Nominal design and Design centering. The Feasibility design layer employs an artificial neural network to generate an initial solution. The following two layers employ genetic algorithms to optimize the amplifier for performance and yield respectively. The proposed design methodology is illustrated on the design of a folded-cascode operational transconductance amplifier and is validated by the results of extensive simulation.

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An Evolutionary Algorithm-Based Approach to Robust Analog Circuit Design using Constrained Multi-Objective Optimization

Cited by 13 publications

References 10 publications

New results in modelling derived from Bayesian filtering

New results in modelling derived from Bayesian filtering

Multi-objective optimization of doping profile in semiconductor design

An evolutionary approach for Nominal design and yield enhancement of analog amplifiers

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