Automated inspection of microlens arrays - CiteSeerX
Bibliography I. P. Nussbaumy, R. Voelkel, H.-P. Herzig, M. Eisner, and. S. Haselbeck. Design, fabrication and testing of microlens arrays for sensors and ...
Automated inspection of microlens arrays James Mure-Dubois and Heinz H¨ugli University of Neuchˆ atel Institute of Microtechnology, 2000 Neuchˆ atel, Switzerland
Optical and Digital Image Processing - 07.04.2008
Outline 1
Microlens arrays inspection
2
Inspection methods and comparison Reference subtraction Blob analysis
3
Defect detection based on blob analysis
4
Semi-automated inspection system
5
Conclusion
J.Mure-Dubois/ 07.04.2008
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Outline 1
Microlens arrays inspection
2
Inspection methods and comparison Reference subtraction Blob analysis
3
Defect detection based on blob analysis
4
Semi-automated inspection system
5
Conclusion
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Microlens arrays Optical devices combining many small lenses. Used for collimation, illumination, imaging[?] . . . Specificities for this work: Small lenses : 10 ≤ d ≤ 50 µm. Gaps coated with metal. Device with more than 2000000 lenses!
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Inspection - Array defects
No defect
Filament on array
Missing metal
Metal covering
Bad lens
Defects combination
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Semi-automated inspection system
The number of images to inspect is large. Human inspection is slow and reliability is low. Most images contain no defects. Automated defect detection can speed-up the inspection.
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Outline 1
Microlens arrays inspection
2
Inspection methods and comparison Reference subtraction Blob analysis
3
Defect detection based on blob analysis
4
Semi-automated inspection system
5
Conclusion
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Reference subtraction
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−
It
Advantages : Short processing time. Low memory requirements. J.Mure-Dubois/ 07.04.2008
Ir
| =
Disadvantages : Requires accurate alignment. Sensitive to coarse sampling. -8 -
Id
Alignment and coarse sampling issue
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Blob analysis
Advantages : Insensitive to alignment and coarse sampling. Simple, parametric lens models can be used. Easily adapted to new lens array geometry. Disadvantage : Segmentation is critical.
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Methods comparison
Reference sub. Illumination may vary (gradients + − vignetting) No alignment between array lattice −− and image axes Defects may vary greatly in size ++ and intensity characteristics Short processing time (< 1 s) ++ Challenge
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Blob analysis 0 ++ ++ +
Outline 1
Microlens arrays inspection
2
Inspection methods and comparison Reference subtraction Blob analysis
3
Defect detection based on blob analysis
4
Semi-automated inspection system
5
Conclusion
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Blob analysis - Process
Input
Segmentation
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Morphology Labeling
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Defect detection
Segmentation
Global threshold θ
Lens regions J.Mure-Dubois/ 07.04.2008
Metal + lens top regions - 14 -
Morphology and labeling Lens regions
Metal + lens top regions
Denoising: Opening with 3x3 kernel Labeling: V8 connected regions
Labeling: V8 connected regions Removal of largest region (metal)
Lens blobs
Lens top blobs
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Blob area analysis Lens blobs
Lens top blobs
Area check: Amin,l ≤ Al ≤ Amax,l
Area check: Am ≤ Amax,m
Defects map J.Mure-Dubois/ 07.04.2008
Composite output - 16 -
Outline 1
Microlens arrays inspection
2
Inspection methods and comparison Reference subtraction Blob analysis
3
Defect detection based on blob analysis
4
Semi-automated inspection system
5
Conclusion
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Semi-automated inspection system
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Blob area - Implementation The defect detection module is implemented in Matlab and uses the Image Processing Toolbox. Parameters considered: segmentation intensity → segmThr lens area → minArea, maxArea maximum hole area → maxWhiteArea
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Blob area - Defect detection
maxLensArea maxLensArea
maxLensArea
minLensArea maxMetalArea
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maxLensArea maxLensArea
minLensArea maxMetalArea
Blob area - Results
Test image
J.Mure-Dubois/ 07.04.2008
Composite output
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Blob area - Results
Test image
J.Mure-Dubois/ 07.04.2008
Composite output
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Blob area - Results
Test image
J.Mure-Dubois/ 07.04.2008
Composite output
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Blob area - Results
Test image
J.Mure-Dubois/ 07.04.2008
Composite output
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Blob area - Performance Tests carried out on devices with a high number of defects. A B Device Images acquired 1804 1804 Defect detected automatically 446 242 Independent human annotation Defects found 133 58 False positive rate 17.4% 10.2% False negative rate 0% 0% Semi-automated human annotation Defects found 433 242 False positive rate 0.72% 0% False negative rate 0% 0% J.Mure-Dubois/ 07.04.2008
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Outline 1
Microlens arrays inspection
2
Inspection methods and comparison Reference subtraction Blob analysis
3
Defect detection based on blob analysis
4
Semi-automated inspection system
5
Conclusion
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Conclusions Image processing methods enabling automation of microlens arrays inspection were studied An automated defect detection system was realized, based on a blob analysis method Tests confirm that no defect goes through the system. Tests show a low false positive rate: the human supervisor is freed from the burden of watching large series of defect free images. Possible improvements: Automatic parameter generation from reference images Smarter segmentation methods (gradient based)
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Acknowledgments
The authors would like to thank B. Putz and K. Weible at SUSS MicroOptics, for providing the annotated test image databases.
Thank you for your attention !
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Bibliography I
P. Nussbaumy, R. Voelkel, H.-P. Herzig, M. Eisner, and S. Haselbeck. Design, fabrication and testing of microlens arrays for sensors and microsystems. Pure Appl. Opt., 6:617–636, 1997.
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