Automated visual inspection: 1981 to 1987
Computer Vision, Graphics, and Image Processing
The P300: a system for automatic patterned wafer inspection
Machine Vision and Applications
Recent advances in the automatic inspection of integrated circuits for pattern defects
Machine Vision and Applications
A survey of automated visual inspection
Computer Vision and Image Understanding
Automatic PCB inspection algorithms: a survey
Computer Vision and Image Understanding
A subpattern level inspection system for printed circuit boards
Computer Vision and Image Understanding
Segmentation of printed circuit board images into basic patterns
Computer Vision and Image Understanding
A golden-template self-generating method for patterned wafer inspection
Machine Vision and Applications
Defect detection in patterned wafers using anisotropic kernels
Machine Vision and Applications
Novelty detection for the inspection of light-emitting diodes
Expert Systems with Applications: An International Journal
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This paper presents a novel technique for detecting possible defects in two-dimensional wafer images with repetitive patterns using prior knowledge. The technique has a learning ability that can create a golden-block database from the wafer image itself, then modify and refine its content when used in further inspections. The extracted building block is stored as a golden block for the detected pattern. When new wafer images with the same periodical pattern arrive, we do not have to recalculate their periods and building blocks. A new building block can be derived directly from the existing golden block after eliminating alignment differences. If the newly derived building block has better quality than the stored golden block, then the golden block is replaced with the new building block. With the proposed algorithm, our implementation shows that a significant amount of processing time is saved. Also, the storage overhead of golden templates is reduced significantly by storing golden blocks only.