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Updated: Aug 24, 2025

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Quantitative Cathodoluminescence Mapping: A CdMgSeTe Thin-Film Case Study.

Aida Torabi¹, James Sullivan¹, Carey Reich²

¹Department of Science and Mathematics, Texas A&M University-Central Texas, Killeen, Texas 76549, United States.

|October 24, 2022

Summary

This study introduces a new R-language algorithm for analyzing cathodoluminescence (CL) spectra, offering quantitative insights into semiconductor thin film properties beyond traditional color mapping. The method reveals film heterogeneity through statistical luminescence analysis.

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Area of Science:

Materials Science
Solid State Physics
Optoelectronics

Background:

Cathodoluminescence (CL) mapping is crucial for measuring semiconductor thin film band gaps.
Current analysis often relies on qualitative color mapping, limiting detailed property assessment.
A need exists for advanced analytical methods to extract comprehensive data from CL spectra.

Purpose of the Study:

To develop and apply a novel algorithm for the functionalization, analysis, and statistical measurement of luminescence data from CL mapping.
To provide deeper insights into the properties of semiconductor thin films by moving beyond traditional color mapping.
To demonstrate the algorithm's utility using Cadmium Magnesium Selenide Telluride (CdMgSeTe) films as a case study.

Main Methods:

Development of a data analysis algorithm coded in the R programming language.
Application of the algorithm to analyze full-spectrum CL maps of CdMgSeTe thin films.
Utilizing statistical analysis of luminescence intensity, wavelength, spectra type curves, peak wavelength distributions, and relative intensity maps.

Main Results:

The algorithm successfully generated quantitative statistical measurements from CL data.
Heterogeneity in the CdMgSeTe films was effectively quantified through various statistical analyses.
The method provided additional insights into film properties compared to standard color mapping techniques.

Conclusions:

The developed R-language algorithm enhances the analysis of CL data, enabling quantitative assessment of semiconductor film properties.
This approach offers a more comprehensive understanding of film heterogeneity and luminescent characteristics.
The methodology has broad potential applications for the characterization of diverse semiconductor thin films.