Rutherford Backscattering Spectrometry (RBS) is an important technique providing elemental information of the near surface region of samples with high accuracy and robustness. However, this technique lacks throughput by the limited rate of data processing and is hardly routinely applied in research with a massive number of samples (i.e. hundreds or even thousands of samples). The situation is even worse for complex samples. If roughness or porosity is present in those samples the simulation of such structures is computationally demanding. Fortunately, Artificial Neural Networks (ANN) show to be a great ally for massive data processing of ion beam data. In this paper, we report the performance comparison of ANN against human evaluation and an automatic fit routine running on batch mode. 500 spectra of marker layers from the stellarator W7-X were used as study case. The results showed ANN as more accurate than humans and more efficient than automatic fits.

卢瑟福背散射光谱法（RBS）是一项重要技术，可提供具有高准确性和鲁棒性的样品近表面区域的元素信息。然而，该技术由于有限的数据处理速率而缺乏通量，并且几乎没有常规应用于具有大量样本（即数百甚至数千个样本）的研究中。对于复杂的样本，情况甚至更糟。如果那些样品中存在粗糙度或孔隙度，则对这种结构的模拟在计算上是有要求的。幸运的是，人工神经网络（ANN）是离子束数据海量数据处理的绝佳盟友。在本文中，我们报告了人工神经网络与人工评估以及在批处理模式下运行的自动拟合例程的性能比较。研究者使用了来自恒星器W7-X的500个标记层的光谱。结果表明，人工神经网络比人类更准确，并且比自动拟合更有效。