automated in-line defect classification and localization in solar cells for laser-based repair

Automated in-Line Defect Classification and Localization in Solar Cells for Laser-Based Repair

Jorge Rodríguez –Araújo, Antón García-Díaz

AIMEN Technology Center, Porriño, Spain

ISIE 2014, Istambul, 2-6-2014

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Index

1. Motivation and Innovative Character.

2. Proposed Solution.

3. Diagnostic and Defect Segmentation.

4. Repair Process Decision.

5. Experimental Results.

6. Conclusions and future work.

Index

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Motivation and Innovative Character

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Motivation and Innovative Character

• Solar cells are made of silicon (156x156mm).• Monocrystalline and Polycrystalline silicon (more common materials).

• The manufacturing process produces defects.

Photovoltaic solar cells

busbars

front back

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Motivation and Innovative Character

• Electroluminescence imaging.

• Shunts and Cracks (most important).(> 50%) • Lacks of metallization (less frequent) (< 20%).• Finger interruptions (reduced effects). (> 58%)

• Shunts and Cracks may be repaired.• Cutting or isolating using laser technology.

• New pieces of cells are obtained.

Defects

Detection

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Proposed Solution

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Proposed Solution

• Laser Repair System (laser process).• Defects Inspector (defects identification and laser control).• Data Input Block (electroluminescence provider).

Solar cell repair system

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Proposed Solution

• Cell Diagnostic (defects segmentation from EL).• Repairing Decision (laser process decision).• Cell Alignment (laser process correction).

Defects inspector

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Diagnostic and Defect Segmentation

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Diagnostic and Defect Segmentation

• Human experts examine EL images.– Changes on texture.– Changes on the shape of texture boundaries.

• Defect Diagnostic (texture based)– Decomposition: for automatic features generation.– Adaptation: for enhancement of features.– Pixel level classification: for multiclass identification.

Bio-inspired texture approach

Texture approach

Defects

Log Gabor filters

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Diagnostic and Defect Segmentation

• Trained with only 4 images.– 4 cracks presents.– 4 shunts presents.

• Type of defect and boundary contour are identified.

Some examples of identified defects.

Pixel level identification

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Repair Process Decision

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Repair Process Decision

• Decision rules:.– Isolate shunts not on a busbar.– Cut shunts on a busbar or close to one.– Cut pieces to remove cracks.

Solar cell repair decision

Examples of repair decision.

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Repair Process Decision

• Closing morphological operation.• Inversion and thresholding.• Edge detection and contour.• Bounding box localization.

Cell location and alignment

Steps on cell localization.

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Experimental results

Experimental Results

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Laser-based repair unit

Camera perspective.

Scanner working area.

Laser repair system.

Experimental results

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Laser scanner calibration

Galvo-scanner working area.

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Experimental results

Calibration pictures

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Experimental results

Processing pictures

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Experimental results

Results pictures

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Experimental Results

• 47 cells considered.• 45 cells repaired.• 69% waste reduction.

Processing results

Defect type

# of Defects

TP FP FN Repaired

Shunts 43 43 4 0 42

Cracks 38 36 6 2 36

All 81 79 10 2 45*

* This value refers to the number of cells repaired.

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Conclusions and Future Work

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Conclusions and Future work

• Defect segmentation and classification.• In-line solar cells repair system.• Able to isolate and cut defects.• 69% of rejected cells reutilization.

• Discriminate more defects, like metallization.• More complex repair strategies.• Isolation based on efficiency estimation.

Conclusions

Future work

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AIMEN – Central y Laboratoriosc/ Relva 27 A

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Thank you for your attentionJorge Rodríguez Araujo | Research Engineer

Ph +34 986 344 000 | jorge.rodriguez@aimen.es