Nanotech 2004 Vol. 2
Nanotech 2004 Vol. 2
Technical Proceedings of the 2004 NSTI Nanotechnology Conference and Trade Show, Volume 2

Nano Scale Device Modeling Chapter 2

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, France

Pages: 21 - 24

Keywords: arsenic activation,modelling, calibration, DoE, optimization, genetic algorithm, analysis of variance

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.

ISBN: 0-9728422-8-4
Pages: 519
Hardcopy: $79.95