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Revealing the Impact of CZTSe/CdS Interface Fluctuations on PV Device Performance through Big Data Analysis Assisted
Jon Garí-Galíndez1,2, Fabien Atlan1, Jacob Andrade-Arvizu1
1Catalonia Institute for Energy Research - IREC, Sant Adrià de Besòs, Barcelona, 08930, Spain.
Small Methods
|March 21, 2025
Summary
Developing advanced inspection methods with machine learning is key for scaling thin film solar cells. Subtle defects in the CdS layer, not the absorber, limit efficiency, with potential for a 2% gain.
Area of Science:
- Materials Science
- Renewable Energy
- Photovoltaics
Background:
- Industrial scale-up of thin film solar cells requires robust quality control.
- Understanding subtle material inhomogeneities is crucial for optimizing device performance.
Purpose of the Study:
- To develop and demonstrate an inspection methodology using machine learning for analyzing thin film solar cell scale-up.
- To identify the primary factors limiting device efficiency in Cu2ZnSnSe4/CdS solar cells.
Main Methods:
- Generation of a large dataset from Raman and photoluminescence spectroscopy, and J-V measurements.
- Application of statistical analysis (spectral difference) and machine learning (multivariate polynomial regressions) on ≈400 individual cells across two large-area samples.
Main Results:
- Identified subtle nanostructure and surface defects in the CdS layer as the main performance limitation.
- Ruled out compositional fluctuations or defects in the kesterite absorber as primary limiting factors.
Conclusions:
- The CdS layer's surface and nanostructure quality significantly impacts thin film solar cell efficiency.
- Addressing CdS layer defects could lead to an absolute 2% increase in device efficiency, advancing kesterite solar cell technology.
