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Color Simulation of Multilayered Thin Films Using Python.

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Názov: Color Simulation of Multilayered Thin Films Using Python.
Autori: Lee, Dongik, Lee, Seunghun
Zdroj: Applied Sciences (2076-3417); May2025, Vol. 15 Issue 9, p4814, 15p
Predmety: MULTILAYERED thin films, RADIOFREQUENCY sputtering, ZINC oxide films, SUBSTRATES (Materials science), THIN films
Abstrakt: Physical insight into a material can be first gained by its color, as the reflectance spectrum of an object reflects its microstructure and complex refractive indices. Here, we present a comprehensive overview of electrodynamics and optics related to reflectance spectra and color. We provide an open-source Python code for simulating reflectance spectra and extracting color values. The validity and applicability of the code are demonstrated through a comparative analysis with both the literature and experimental data. For SnO2 and ZnO thin films deposited on SiO2/Si substrates using rf sputtering, the Python code and simulation predict color variations with the film thickness and effectively capture their angular dependence. This code will help in understanding and making use of color-related phenomena. It can be further used and developed for various purposes, particularly machine learning, which requires extensive spectral and color data for model training. [ABSTRACT FROM AUTHOR]
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Abstrakt:Physical insight into a material can be first gained by its color, as the reflectance spectrum of an object reflects its microstructure and complex refractive indices. Here, we present a comprehensive overview of electrodynamics and optics related to reflectance spectra and color. We provide an open-source Python code for simulating reflectance spectra and extracting color values. The validity and applicability of the code are demonstrated through a comparative analysis with both the literature and experimental data. For SnO<subscript>2</subscript> and ZnO thin films deposited on SiO<subscript>2</subscript>/Si substrates using rf sputtering, the Python code and simulation predict color variations with the film thickness and effectively capture their angular dependence. This code will help in understanding and making use of color-related phenomena. It can be further used and developed for various purposes, particularly machine learning, which requires extensive spectral and color data for model training. [ABSTRACT FROM AUTHOR]
ISSN:20763417
DOI:10.3390/app15094814