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Detection of pinhole defects on chips and wafers using DCT enhancement in computer vision systems
by
Lin, Hong-Dar
, Ho, Duan-Cheng
in
Barrier layers
/ Batch production
/ Computer vision
/ Decomposition
/ Defects
/ Discrete cosine transform
/ Feature extraction
/ Filtration
/ Image detection
/ Image restoration
/ Inspection
/ Passive components
/ Pinhole defects
/ Template matching
/ Vision systems
2007
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Detection of pinhole defects on chips and wafers using DCT enhancement in computer vision systems
by
Lin, Hong-Dar
, Ho, Duan-Cheng
in
Barrier layers
/ Batch production
/ Computer vision
/ Decomposition
/ Defects
/ Discrete cosine transform
/ Feature extraction
/ Filtration
/ Image detection
/ Image restoration
/ Inspection
/ Passive components
/ Pinhole defects
/ Template matching
/ Vision systems
2007
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Do you wish to request the book?
Detection of pinhole defects on chips and wafers using DCT enhancement in computer vision systems
by
Lin, Hong-Dar
, Ho, Duan-Cheng
in
Barrier layers
/ Batch production
/ Computer vision
/ Decomposition
/ Defects
/ Discrete cosine transform
/ Feature extraction
/ Filtration
/ Image detection
/ Image restoration
/ Inspection
/ Passive components
/ Pinhole defects
/ Template matching
/ Vision systems
2007
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Detection of pinhole defects on chips and wafers using DCT enhancement in computer vision systems
Journal Article
Detection of pinhole defects on chips and wafers using DCT enhancement in computer vision systems
2007
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Overview
This paper presents a global approach for the automatic inspection of tiny pinhole defects in randomly textured surfaces of surface barrier layer (SBL) chips. By means of a discrete cosine transform (DCT)-based image restoration scheme, the proposed method is independent of textural features and thus not confined by the limitations of feature extraction based methods. Through properly decomposing the frequency matrix of an image in the DCT domain and selecting the best radius of the sector filter for the high-pass filtering operation, we effectively attenuate the global random texture pattern and accentuate only tiny pinhole defects in the restored image. We also develop two accumulative sum detection procedures that automatically determine the best high-pass filtering parameters based on the abrupt changes of the frequency coefficients in the decomposed matrix. Experimental results show that the proposed method outperforms the traditional approach in reducing the Type I error by 70–80% and in decreasing the deviation of the defect areas by 95%. Moreover, the proposed method can be applied to various types of passive components in large-batch production because no precise positioning of the target chip or template matching is required.
Publisher
Springer Nature B.V
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