Automatic defect classification for semiconductor manufacturing
Machine Vision and Applications
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Pattern Recognition Letters - Special issue: Artificial neural networks in pattern recognition
Data mining for yield enhancement in semiconductor manufacturing and an empirical study
Expert Systems with Applications: An International Journal
Clustered defect detection of high quality chips using self-supervised multilayer perceptron
Expert Systems with Applications: An International Journal
Defect spatial pattern recognition using a hybrid SOM-SVM approach in semiconductor manufacturing
Expert Systems with Applications: An International Journal
Separation of composite defect patterns on wafer bin map using support vector clustering
Expert Systems with Applications: An International Journal
Neural Computing and Applications
Computers and Industrial Engineering
Adaptive virtual support vector machine for reliability analysis of high-dimensional problems
Structural and Multidisciplinary Optimization
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Optical inspection techniques have been widely used in industry as they are non-destructive, efficient to achieve, easy to process, and can provide rich information on product quality. Defect patterns such as rings, semi-circles, scratches, clusters are the most common defects in the semiconductor industry. Most methods cannot identify two scale-variant or shift-variant or rotation-variant defect patterns, which in fact belong to the same failure causes. To address these problems, a new approach has been proposed in this paper to detect these defect patterns in noisy images obtained from printed circuit boards, wafers, and etc. A median filter, background removal, morphological operation, segmentation and labeling are employed in the detection stage of our method. Support vector machine (SVM) is used to identify the defect patterns which are resized. Classification results of both simulated data and real noisy raw data show the effectiveness of our method.