Recent advances in the automatic inspection of integrated circuits for pattern defects
Machine Vision and Applications
The nature of statistical learning theory
The nature of statistical learning theory
A golden-template self-generating method for patterned wafer inspection
Machine Vision and Applications
A golden-block-based self-refining scheme for repetitive patterned wafer inspections
Machine Vision and Applications
WACV '02 Proceedings of the Sixth IEEE Workshop on Applications of Computer Vision
A quantile-quantile plot based pattern matching for defect detection
Pattern Recognition Letters
Non-stationary analysis on datasets and applications
Non-stationary analysis on datasets and applications
Defect detection on semiconductor wafer surfaces
Microelectronic Engineering
An eigenvalue-based similarity measure and its application in defect detection
Image and Vision Computing
Defect detection in patterned wafers using anisotropic kernels
Machine Vision and Applications
Color and texture image retrieval using chromaticity histograms and wavelet frames
IEEE Transactions on Multimedia
Input space versus feature space in kernel-based methods
IEEE Transactions on Neural Networks
Nonlinear kernel-based statistical pattern analysis
IEEE Transactions on Neural Networks
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Recent computational methods of wafer defect detection often inspect Scanning Electron Microscope (SEM) images of the wafer. In this paper, we propose a kernel-based approach to multichannel defect detection, which relies on simultaneous acquisition of three different images for each sample in a SEM tool. The reconstruction of a source patch from reference patches in the three channels is constrained by a similarity criterion across the three SEM images. The improved performance of the proposed algorithm is demonstrated, compared to a single-channel kernel-based defect detection method.