Improvement of Multi-Lines Bridge Defect Classification by Hierarchical Architecture in Artificial Intelligence Automatic Defect Classification

Defect classifications are the very important steps as the in-line defect inspection of the semiconductor manufacturing procedure. The conventional defect classifications are usually through visual judgement by engineer or technical assistant. However, it's time-consuming and laborious. In our...

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Bibliographic Details
Published in:IEEE transactions on semiconductor manufacturing 2021-08, Vol.34 (3), p.346-351
Main Authors: Lin, Bing-Sheng, Cheng, Jung-Syuan, Liao, Hsiang-Chou, Yang, Ling-Wu, Yang, Tahone, Chen, Kuang-Chao
Format: Article
Language:English
Subjects:
Artificial intelligence
Automatic defect classification
Bridges
Classification
Cluster analysis
Clustering
Convolution
convolutional neural network
Dislocations
Feature extraction
hierarchical structure
Inspection
Machine learning
Morphology
multi-lines bridge
Purity
residual neural network
Residual neural networks
scanning electron microscopy
Stability
Structural hierarchy
Testing
Training
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Summary:Defect classifications are the very important steps as the in-line defect inspection of the semiconductor manufacturing procedure. The conventional defect classifications are usually through visual judgement by engineer or technical assistant. However, it's time-consuming and laborious. In our recent study, the artificial intelligence automatic defect classification (AI-ADC) performed promisingly good accuracy and purity (A/P) of the auto defect classification by deep learning method. Nevertheless, some kinds of tiny defects are not only suffered lower A/P issues, but also suffered bad A/P stability of real defect classification. In this work, we propose the novel method, called "Hierarchical structure AI-ADC", which introduced a second binning classifier and it's based on hierarchical clustering to achieve more precise defect classification. As a result, the proposed method shown obvious improvements to the binning purity of multi-lines bridge defect from 56% to 88% as well as the stability variation has been reduced from 55% to 22%, besides it also can be applied to classify the similar defect types efficiently. Indeed this approach achieves excellent defect classification and highly stable performance.
ISSN:0894-6507
1558-2345
DOI:10.1109/TSM.2021.3076808