Analytical decision boundary feature extraction for neural networks for the recognition of unconstrained handwritten digits

During an electrical testing stage, each die on a wafer must be tested to determine whether it functions as it was originally designed. In the case of a clustered defect on the wafer, such as scratches, stains, or localized failed pattems, the tester may not detect all of the defective dies in the flawed area. To avoid the defective dies proceeding to final assembly, an existing tool is currently used by a testing faclory to detect the defect cluster and mark all the defective dies in the flawed region or close to the flawed region; otherwise, the testing factory must assign five to ten workers to check the wafers and hand mark the defective dies. This paper proposes two new wafer-scale defect cluster identifiers to detect the defect clusters, and compares them with the existing tool used in the industry. The experimental results verify that one of the proposed algorithms is very effective in defect identification and achieves better performance than the existing tool.

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Reliable Recognition of Handwritten Digits Using Hamming Network

Archana¹,

Madhavi

Muralikrishna

2016

2016 IEEE 6th International Conference on Advanced Computing (IACC)

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Analytical decision boundary feature extraction for neural networks for the recognition of unconstrained handwritten digits

Cited by 4 publications

References 10 publications

Clustered defect detection of high quality chips using self-supervised multilayer perceptron

Clustered defect detection of high quality chips using self-supervised multilayer perceptron

Application of neural networks and filtered back projection to wafer defect cluster identification

Reliable Recognition of Handwritten Digits Using Hamming Network

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