refellips: A Python package for the analysis of variable angle spectroscopic ellipsometry data

refellips is an open-source analysis package written in Python for modelling variable angle spectroscopic ellipsometry data. The software is designed to be used in Jupyter notebook environments or simple Python scripts, facilitating reproducible research. The modular design of refellips means users...

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Veröffentlicht in:SoftwareX Jg. 20; S. 101225
Hauptverfasser: Robertson, Hayden, Gresham, Isaac J., Prescott, Stuart W., Webber, Grant B., Wanless, Erica J., Nelson, Andrew
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier B.V 01.12.2022
Elsevier
Schlagworte:
Analysis
Ellipsometry
Modelling
Open source
Reproducibility
Thin films
74A50
Thin films
Reproducibility
Analysis
Modelling
Open source
Ellipsometry
ISSN:2352-7110, 2352-7110
Online-Zugang:Volltext
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Beschreibung
Zusammenfassung:refellips is an open-source analysis package written in Python for modelling variable angle spectroscopic ellipsometry data. The software is designed to be used in Jupyter notebook environments or simple Python scripts, facilitating reproducible research. The modular design of refellips means users can implement simple models (e.g., slabs) or create their own complex mathematical optical models to describe an interface. refellips can read a range of file types from common ellipsometers and utilises the suite of local and global minimisers offered by the scipy package to fit data. refellips allows for the batch processing of large ellipsometry datasets, e.g., from the spatial mapping of surfaces or time-series experiments. Furthermore, as refellips is part of the refnx family, its operation (e.g., model creation) is almost identical to that used by refnx for neutron and X-ray reflectometry analyses, permitting simultaneous co-refinement of ellipsometry, neutron, and X-ray reflectometry data for the first time. This enables superior characterisation of complex interfaces in condensed matter films and oxide surfaces. [Display omitted]
ISSN:2352-7110
2352-7110
DOI:10.1016/j.softx.2022.101225