Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Discrete cosine transform: algorithms, advantages, applications
Discrete cosine transform: algorithms, advantages, applications
Fundamentals of statistical signal processing: estimation theory
Fundamentals of statistical signal processing: estimation theory
An introduction to signal detection and estimation (2nd ed.)
An introduction to signal detection and estimation (2nd ed.)
Recent advances in the automatic inspection of integrated circuits for pattern defects
Machine Vision and Applications
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
Evaluation of global image thresholding for change detection
Pattern Recognition Letters
Anomaly detection based on an iterative local statistics approach
Signal Processing
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
De-noising by soft-thresholding
IEEE Transactions on Information Theory
Image change detection algorithms: a systematic survey
IEEE Transactions on Image Processing
Novelty detection for the inspection of light-emitting diodes
Expert Systems with Applications: An International Journal
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Wafer defect detection often relies on accurate image registration of source and reference images obtained from neighboring dies. Unfortunately, perfect registration is generally impossible, due to pattern variations between the source and reference images. In this paper, we propose a defect detection procedure, which avoids image registration and is robust to pattern variations. The proposed method is based on anisotropic kernel reconstruction of the source image using the reference image. The source and reference images are mapped into a feature space, where every feature with origin in the source image is estimated by a weighted sum of neighboring features from the reference image. The set of neighboring features is determined according to the spatial neighborhood in the original image space, and the weights are calculated from exponential distance similarity function. We show that features originating from defect regions are not reconstructible from the reference image, and hence can be identified. The performance of the proposed algorithm is evaluated and its advantage is demonstrated compared to using an anomaly detection algorithm.