RBS without humans

We present an algorithm based on artificial neural networks (ANNs) able to determine optimised experimental conditions for Rutherford backscattering (RBS) measurements of Ge-implanted Si. The algorithm can be implemented for any element implanted into a lighter substrate, and can be extended to other ion beam analysis techniques. It is a push-button black box, and does not require any human intervention. It is suited for automated control of an experimental setup, given an interface to the relevant hardware. Once the experimental conditions are optimised, the algorithm analyses the final data obtained, and determines the desired parameters. The method is hence also suited for automated analysis of the data