Bias and synergy in the self-consistent approach of data analysis of ion beam techniques

Abstract

Using multiple ion beam analysis measurements or techniques combined with self-consistent data processing generally allows for extracting more (or more accurate) information than processing data from single measurements separately. Solving ambiguities, improving the final depth resolution, defining constraints and extending applicability are the main strengths of the data-fusion approach, which essentially consists in formulating a multi-objective minimization problem that can be tackled by the adoption of the weighted-sum method. A simulation study is reported in order to evaluate the systematic error inserted in the analysis by the choice of a specific objective function, or even by the weights or normalization adopted in the weighted-sum method. We demonstrate that the bias of the analysed objective functions asymptotically converges to the true value for better statistics and that the measurement can be ranked by its information content, where some combinations of measurements better constrain the multi-objective optimization.