研究目的
To present a general computational routine that combines Griddler, a finite-element simulator for solar cells, and tailored multivariate regression techniques to seek cell parameters that best explain a set of luminescence imaging data.
研究成果
The Griddler AI analysis routine marks a new paradigm in luminescence image analysis using FEMs and multivariate regression, offering advantages in generalizability, strong representation of physical components, and amenability to further predictions by simulations.
研究不足
The method's accuracy depends on the quality of the luminescence images and the assumptions made in the finite-element model.
1：Experimental Design and Method Selection:
The routine combines Griddler, a finite-element simulator for solar cells, and tailored multivariate regression techniques.
2：Sample Selection and Data Sources:
Applied to a dataset of luminescence images from about 80 monocrystalline silicon Al back surface field solar cells with varying front metal grid contact resistance and recombination.
3：List of Experimental Equipment and Materials:
Griddler, a finite-element simulator for solar cells.
4：Experimental Procedures and Operational Workflow:
The routine is applied to analyze luminescence images under different conditions to extract cell parameters.
5：Data Analysis Methods:
Uses multivariate regression techniques to fit cell parameters to luminescence imaging data.
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