Abstract
In this work, we characterize the performance of a deep convolutional neural network designed to detect and quantify chemical elements in experimental x-ray photoelectron spectroscopy data. Given the lack of a reliable database in literature, in order to train the neural network we computed a large (<100 k) dataset of synthetic spectra, based on randomly generated materials covered with a layer of adventitious carbon. The trained net performs as well as standard methods on a test set of ≈500 well characterized experimental x-ray photoelectron spectra. Fine details about the net layout, the choice of the loss function and the quality assessment strategies are presented and discussed. Given the synthetic nature of the training set, this approach could be applied to the automatization of any photoelectron spectroscopy system, without the need of experimental reference spectra and with a low computational effort.
Highlights
Deep neural networks (DNNs) are currently state-of-the-art in image recognition applications, and have already been tested for several scientific spectroscopy applications[1,2,3,4]
If hν is larger that the binding energy (BE) of the electrons in the solid, the electron is ejected with a kinetic energy KE = hν − BE − φ, where φ is the work function, related to the bulk/vacuum discontinuity at the sample surface
In this work we show the application of a DNN to the task of identification and quantification of X-ray photoelectron spectroscopy (XPS) survey spectra
Summary
Deep neural networks (DNNs) are currently state-of-the-art in image recognition applications, and have already been tested for several scientific spectroscopy applications[1,2,3,4]. X-ray photoelectron spectroscopy (XPS) data represent an ideal application field for deep neural network (DNN) classification methods. If hν is larger that the binding energy (BE) of the electrons in the solid, the electron is ejected with a kinetic energy KE = hν − BE − φ, where φ is the work function, related to the bulk/vacuum discontinuity at the sample surface. This simple relation allows one to collect XPS spectra by measuring the number and the kinetic energy of the photoemitted electrons
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