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23 May 2011 Alternative robust statistical methods to reduce parameters uncertainty: application to scatterometry
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The determination of interesting parameters is very often performed through curves fitting, like in scatterometry for example. Classically, the problem is solved by means of least-squares. Although optimal in an ideal context of Normal errors, this assumption is not valid mainly because of modeling and calibration errors, and induce some uncontrolled biases. In this paper, we propose radically different principles of fitting techniques, based on an Entropy criterion instead of the Maximum Likelihood Principle. Combining simple implementation and high performance, we show that this technique is optimal for pure Normal noise and dramatically reduces bias on parameters for corrupted data, outperforming conventional robust M-Estimators. We also provide tests on real scatterometry samples which demonstrate a bias reduction of a factor 4.
© (2011) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
J. Hazart, F. Sarrazy, R. Buyssou, and C. Dezauzier "Alternative robust statistical methods to reduce parameters uncertainty: application to scatterometry", Proc. SPIE 8083, Modeling Aspects in Optical Metrology III, 80830J (23 May 2011);

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