Optimizing CMOS LNA circuits through multi-objective meta heuristics

2010 XIth International Workshop on Symbolic and Numerical Methods, Modeling and Applications to Circuit Design (SM2ACD) Pub Date : 2010-12-17 DOI:10.1109/SM2ACD.2010.5672305

M. Kotti, A. Sallem, Mariam Bougharriou, M. Fakhfakh, M. Loulou

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引用次数: 18

Abstract

Particle swarm optimization (PSO) has shown to be an efficient, robust and simple optimization algorithm. Recently, the mono-objective version of the PSO algorithm was adapted and used to optimize only one performance of RF circuits, mainly the voltage gain of low noise amplifiers. In this work, we propose to optimize more than one performance function of LNAs while satisfying imposed and inherent constraints. We deal with generating the Pareto front linking two conflicting performances of a LNA, namely the voltage gain and the noise figure. The adopted idea consists of using the symbolic expressions of the scattering parameters ((S21) for the voltage gain, and (S11, S22) for input and output matching). For this purpose we use a Multi-Objective Optimization algorithm PSO incorporating the mechanism of the crowding distance technique (MOPSO-CD). Comparisons with results obtained using NSGA II are presented and ADS simulations, using 0.35µm CMOS technology, are given to show good reached results.

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多目标元启发式优化CMOS LNA电路

粒子群算法是一种简单、高效、鲁棒的优化算法。最近，单目标粒子群算法被用于优化射频电路的一个性能，主要是低噪声放大器的电压增益。在这项工作中，我们提出优化LNAs的多个性能函数，同时满足强加的和固有的约束。我们处理产生帕累托前连接两个相互冲突的性能的LNA，即电压增益和噪声系数。所采用的思想包括使用散射参数的符号表达式((S21)表示电压增益，(S11, S22)表示输入和输出匹配)。为此，我们采用了一种融合了拥挤距离技术机制的多目标优化算法(PSO)。并与采用NSGA II的结果进行了比较，并采用0.35µm CMOS技术进行了ADS仿真，结果表明取得了良好的效果。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

2010 XIth International Workshop on Symbolic and Numerical Methods, Modeling and Applications to Circuit Design (SM2ACD)

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