Self-consistent ion beam analysis: an approach by multi-objective optimization

TL;DR

This paper introduces a multi-objective optimization approach for self-consistent ion beam analysis, combining multiple IBA techniques to improve sample characterization accuracy and reliability.

Contribution

It develops MultiSIMNRA, a new software tool that integrates various optimization algorithms with SIMNRA to achieve consistent analysis of multiple IBA measurements.

Findings

Enhanced sample characterization accuracy.

Reduced ambiguities in analysis results.

Improved confidence in material analysis.

Abstract

Ion Beam Analysis (IBA) comprises a set of analytical techniques suited for material analysis, many of which are rather closely related. Self-consistent analysis of several IBA techniques takes advantage of this close relationship to combine different Ion Beam measurements in a unique model to obtain an improved characterization of the sample. This approach provides a powerful tool to obtain an unequivocal and reliable model of the sample, increasing confidence and reducing ambiguities. 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 uses computational algorithms to minimize an objective function running multiple instances of SIMNRA. With four different optimization algorithms, 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.