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Application of the genetic algorithms in spectroscopic ellipsometry

机译:遗传算法在光谱椭圆仪中的应用

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Calculation of layer thicknesses and optical indexes is the most important part of the spectroscopic ellipsometry experimental data analysis. Most often gradient methods like Levenberg-Marquardt (LM) are used for fitting experimental and calculated characteristics. Unfortunately gradient methods depend on starting values and often get stuck into local minimums. Problems in data fitting arise often when our knowledge about investigated structure is insufficient, e.g. substrates and layers differ from expected, chemical reactions change layers optical structure, model is complicated or contains too many parameters. Such problems, in many cases could be solved with random methods or genetic algorithm (GA) (Surf. Sci. 457 (2000) 157; J. Opt. Soc. Am. A 17 (2000) 129). In this paper combined genetic algorithm is proposed to improve ellipsometric data analysis. This algorithm uses LM method together with GA. Application of the GA in ellipsometry is illustrated with the analysis of the metal-oxide-semiconductor structure.
机译:层厚度和光学指标的计算是椭圆偏振光谱实验数据分析中最重要的部分。最常用的梯度方法(如Levenberg-Marquardt(LM))用于拟合实验和计算出的特征。不幸的是,梯度方法取决于起始值,并且经常陷入局部最小值。当我们对调查结构的知识不足时,例如,在数据拟合中经常会出现问题。基材和层与预期不同,化学反应会改变层的光学结构,模型复杂或包含太多参数。在许多情况下,可以使用随机方法或遗传算法(GA)解决此类问题(Surf。Sci。457(2000)157; J。Opt。Soc。Am。A 17(2000)129)。本文提出了一种组合遗传算法来改进椭偏数据分析。该算法将LM方法与GA结合使用。通过对金属氧化物半导体结构的分析说明了遗传算法在椭圆偏振法中的应用。

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