Premium
GIMPy: a software for the simulation of X‐ray fluorescence and reflectivity of layered materials
Author(s) -
Brigidi Fabio,
Pepponi Giancarlo
Publication year - 2017
Publication title -
x‐ray spectrometry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.447
H-Index - 45
eISSN - 1097-4539
pISSN - 0049-8246
DOI - 10.1002/xrs.2746
Subject(s) - optics , x ray fluorescence , materials science , monochromatic color , semiconductor , thin film , optoelectronics , fluorescence , physics , nanotechnology
X‐ray fluorescence (XRF) analysis is an established technique for quantitative elemental analysis. Grazing incidence X‐ray fluorescence analysis (GIXRF) extends the application of XRF to thin films because of the improved sensibility. GIXRF shares the phenomenological basis with X‐ray reflectivity, a scattering technique typically used for thin‐film metrology, offering sensitivity to elemental depth. This work presents the GIMPy (Grazing Incidence Material analysis with Python) software developed for the analysis of GIXRF spectra by combining a fundamental parameter approach to quantitative XRF analysis and the electric field calculation in stratified media, which also delivers the total reflected intensity as measured in X‐ray reflectivity experiments. An XRF experiment can be modelled from the source, modulation of the primary beam, interactions with a layered sample, absorption of the emitted fluorescence intensities, and the response function of semiconductor energy dispersive detectors obtaining a simulation of the expected spectrum that can be directly compared with the acquired one. The fundamental parameter part includes signal enhancements by cascade effect and secondary fluorescence. The code offers the possibility to take into account the effects originated by deviations from ideal conditions: non‐monochromatic excitation, beam divergence, beam size and shape, sample‐inspected area, and solid angle of detection. The functionality of the code is demonstrated on a set of semiconductor substrates (Si, Ge, and GaAs) and shallow dopant distributions of arsenic in silicon. Copyright © 2017 John Wiley & Sons, Ltd.