Methodology for Prediction of Ultra Shallow Junction Resistivities Considering Uncertainties with a Genetic Algorithm Optimization

	Nanotech 2004 Vol. 2 Technical Proceedings of the 2004 NSTI Nanotechnology Conference and Trade Show, Volume 2 Chapter 2: Nano Scale Device Modeling
	Methodology for Prediction of Ultra Shallow Junction Resistivities Considering Uncertainties with a Genetic Algorithm Optimization
Authors:	C. Renard, P. Scheiblin, F. de Crécy, A. Ferron, E. Guichard, P. Holliger and C. Laviron
Affilation:	CEA-LETI, FR
Pages:	21 - 24
Keywords:	arsenic activation,modelling, calibration, DoE, optimization, genetic algorithm, analysis of variance
Abstract:	The accurate prediction of arsenic activation after spike annealing is mandatory for Ultra Shallow Junction (USJ) sheet resistance optimization for advanced NMOS transistors engineering. For the first time, we propose a fast and efficient methodology which consists in both predicting coefficients which model the arsenic activation, and in calibrating a physically-based mobility model from experimental data. Calibration was obtained by a genetic algorithm optimization of a criterion taking into account the difference between simulation and measurement, and both experimental and modelling uncertainties.
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ISBN:	0-9728422-8-4
Pages:	519
Hardcopy:	$79.95

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