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

www.aimen.es | [email protected] 2

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


Motivation and Innovative Character

4www.aimen.es | [email protected]


• 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



• 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


Proposed Solution


Proposed Solution

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

Solar cell repair system


Proposed Solution

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

Defects inspector


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



• 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


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.



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

Cell location and alignment

Steps on cell localization.


Experimental results

Experimental Results


Laser-based repair unit

Camera perspective.

Scanner working area.

Laser repair system.



Laser scanner calibration

Galvo-scanner working area.



Calibration pictures



Processing pictures



Results pictures


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.


Conclusions and Future Work


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


AIMEN – Central y Laboratoriosc/ Relva 27 A

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Avda. del General Perón, 32, 8 A28020 – MADRID (Madrid)

Telf.+34 687 448 915

Delegación Tecnológica A Coruña

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15190 – A CORUÑA (A Coruña)Telf. +34 617 395 153

Thank you for your attentionJorge Rodríguez Araujo | Research Engineer

Ph +34 986 344 000 | [email protected]

mailto:[email protected]