  Using multiple ion beam analysis measurements, or techniques, combined with
self-consistent data processing, generally allows extracting more (or more
accurate) information from the measurements than processing separately data
from single measurements. Solving ambiguities, improving the final depth
resolution, defining constraints and extending applicability are the main
strengths of the data-fusion approach. It basically 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 analyzed objective functions
asymptotically converges to the true value for better statistics. We also
demonstrate that the joint analysis inherits the accuracy of the most accurate
measurement, establishing a rank of information content, where some
combinations of measurements are more valuable than others, i.e. when processed
together they provide more information by means of a better constraint for the
multi-objective optimization.
