Photovoltaic Cell Luminescence Imaging for Defect Quantification
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Solution Overview
Problem
Existing methods for quality control of photovoltaic cells are inadequate in accurately identifying and quantifying defects, which affect cell performance, and there is a need for a more precise method to detect and remove defective cells early in the manufacturing process.
Innovation Solution
A method involving photoluminescence imaging is used to decompose cell images into defect-free and defect images, calculating a defect quantification parameter through pixel analysis and correction factors, and correlating this with form factor loss to determine cell quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Difficulty of detecting and measuring
If photoluminescence imaging is used to detect defects in photovoltaic cells, then defect detection capability is improved, but measurement precision is insufficient for accurate quantification
Solution Approach 1:
The luminescence image is segmented into multiple components: a first image representing the cell without defects and a second image representing defects. This is achieved by selecting reference pixels from defect-free regions and using their luminescence intensity values to construct the first image, then subtracting it from the original image to obtain the second image containing only defect information.
Solution Approach 2:
The method transforms the raw luminescence image data by changing the parameter representation from absolute intensity values to differential values. By calculating the difference between the original image and the reconstructed defect-free image, the method enhances the visibility and measurability of defect parameters while suppressing background variations.
2Difficulty of detecting and measuring
If image processing is performed to identify defects, then defect identification capability is improved, but processing complexity increases
Solution Approach 1:
The method performs preliminary action by first identifying and extracting reference pixels from known defect-free regions of the cell before processing the entire image. These reference pixels are used to construct a model of the defect-free cell state, which simplifies subsequent defect detection by providing a baseline for comparison.
Solution Approach 2:
The method creates a copy of the defect-free cell state by reconstructing the first image from reference pixel values. This synthetic copy serves as a template that can be subtracted from the original image to reveal defects, avoiding the need for complex pattern recognition algorithms.
3Measurement precision
If luminescence image decomposition is performed to separate defects from cell structure, then defect quantification accuracy is improved, but measurement time increases
Solution Approach 1:
The method applies partial action by selectively processing only the necessary portions of the image data. Instead of analyzing the entire image with complex algorithms, it focuses on extracting and processing only the reference pixels from defect-free regions, then uses these to generate the defect-free model and identify defects through simple subtraction.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables precise identification and removal of defective cells before metallization, improving manufacturing efficiency and reducing waste by ensuring only high-quality cells proceed to further processing.
Implementation Method 1
an excitation step, during which the cell to be controlled is subjected to excitation; a step of acquiring at least one luminescence image of the cell to be controlled after excitation
Data Source
Figure 1~2
Figure 3~4C
Figure 5A
AI summary
The invention relates to a method which includes, for each cell: an excitation step, during which the cell to be monitored is subjected to an excitation with a predetermined level of excitation; a step of acquiring at least one luminescence image of the cell to be monitored after excitation; a step of processing the acquired image; characterised in that, for each cell, a prior step of determining an excitation level that is adjusted to said cell is provided for each cell, the respective adjusted excitation levels of the various cells to be monitored being adapted such that the luminescence intensities of the signals emitted by the various cells are equal to a single reference luminous intensity.