|Track||Date and time||Hall||Duration|
|Contributed Lectures||Wednesday, 17. June 2015., 09:00||Orhideja Hall||20’|
Tiago F. Silva (1), C.L. Rodrigues (1), M. Mayer (2), M.V. Moro (1), G.F. Trindade (1), F.R. Aguirre (1), N. Added (1), M.H. Tabacniks (1)
(1) Instituto de Física da Universidade de São Paulo, Rua do matão, trav. R 187, 05508-090 São Paulo, Brazil.
(2) Max-Planck-Institut für Plasmaphysik, Boltzmannstr. 2, D-85748 Garching, Germany.
Computational tools always played an important role in the data analysis of IBA techniques during its historical development . Many advances occurred in the comprehension of physical processes and measurement system effects, always followed by advances in computational modeling . A recent and well succeeded case is SIMNRA , which is a software widely adopted by the IBA community. Its strength lies on trusted modeling of the physical processes involved in the scattering calculation and measurements system effects. It was recently reported that upgrades can be expected for its new version concerning the skewness of energy spread distributions, improved handling of reaction cross-sections with structure, generalized layer roughness, and sample porosity . Another important software is DataFurnance, that uses advanced algorithms to minimize an objective function to fit energy spectra in scattering analysis . The DataFurnance software is able to handle simultaneously and self-consistently different IBA spectra obtained by multiple techniques applied in the same sample [6,7]. This approach ensures the reliable and unequivocal modeling of the sample , but equally important, the self-consistent analysis inherits the accuracy of the most accurate component of the analysis [8,9]. SIMNRA still remains the most cited software according to reference . Taking advantage of the recognized reliability and quality of the simulations provided by SIMNRA, we developed a multi-process program for a self-consistent analysis based on SIMNRA calculations . MultiSIMNRA also uses computational algorithms to minimize an objective function running multiple instances of SIMNRA. With four different optimization algorithms available, the code can handle sample and setup parameters (including correlations and constraints), to find the set of parameters that best fits simultaneously all experimental data. MultiSIMNRA has been tested in several situations, ranging from the analysis of simple multi-element thin films, up to very complicated multi-layered samples, that included 35 independent spectra being fitted simultaneously. The extreme case of 120 spectra generated by simulations were fitted as test, showing that MultiSIMNRA can handle a massive number of spectra simultaneously and self-consistently.
 Rauhala E.,NIM B 244, 2006, 436-456.
 Mayer M., NIM B 269, 2011, 3006-3013.
 Mayer M., AIP Conference Proceedings 475, 1999, 541.
 Mayer M., NIM B 332, 2014, 176-180.
 Barradas N.P., et.al, NIM B 266, 2008, 1875-1879.
 Barradas N.P., NIM B 161, 2000, 308-313.
 Jeynes C., et.al, NIM B 271, 2012, 107-118.
 Colaux J. L., Anal. Methods 6, 2014, 120-129.
 Jeynes C., Anal. Chem. 84, 2012, 6061–6069.
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