Artificial neural network analysis of RBS data of Er-implanted sapphire

Abstract

Rutherford backscattering spectrometry (RBS) is a well-established technique for the elemental depth profile of the surface layers of samples, including the determination of the dose and depth of implanted elements. We have developed a code based on artificial neural networks (ANN) to analyse RBS data. The ANN was trained using the traditional backpropagation algorithm, which is designed to minimise the average error on a training set of generated data. The algorithm was applied to one important particular case, namely the determination of the amount of Er implanted in sapphire samples, and the depth at which the Er is located. The Er fluence was between 8×10 13 Er +/cm 2 and 2×10 16 Er +/cm 2, for implant energies of 200 and 800 keV. The analysis is instantaneous, automated, and requires absolutely no knowledge from the user aside the experimental conditions. The results obtained are hence well-suited for on-line data analysis.