Semi-Supervised Framework for Wafer Defect Pattern Recognition with Enhanced Labeling

2021 ◽  
Author(s):  
Leon Li-Yang Chen ◽  
Katherine Shu-Min Li ◽  
Xu-Hao Jiang ◽  
Sying-Jyan Wang ◽  
Andrew Yi-Ann Huang ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Defect Pattern

Unsupervised Learning for Wafer Surface Defect Pattern Recognition

2021 ◽  
pp. 275-285
Author(s):  
Sheng Geng ◽  
Huaping Liu ◽  
Feng Wang ◽  
Shimin Zhao ◽  
Hu Liu
Keyword(s):  
Pattern Recognition ◽  
Unsupervised Learning ◽  
Surface Defect ◽  
Wafer Surface ◽  
Defect Pattern

scholarly journals Defect Pattern Recognition Based on Partial Discharge Characteristics of Oil-Pressboard Insulation for UHVDC Converter Transformer

Energies ◽  
2018 ◽  
Vol 11 (3) ◽  
pp. 592 ◽  
Author(s):  
Wen Si ◽  
Simeng Li ◽  
Huaishuo Xiao ◽  
Qingquan Li ◽  
Yalin Shi ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Partial Discharge ◽  
Discharge Characteristics ◽  
Defect Pattern

Deformable Convolutional Networks for Efficient Mixed-Type Wafer Defect Pattern Recognition

2020 ◽  
Vol 33 (4) ◽  
pp. 587-596
Author(s):  
Junliang Wang ◽  
Chuqiao Xu ◽  
Zhengliang Yang ◽  
Jie Zhang ◽  
Xiaoou Li
Keyword(s):  
Pattern Recognition ◽  
Mixed Type ◽  
Convolutional Networks ◽  
Defect Pattern

Defects Identification of Partial Discharge Map in GIS

2013 ◽  
Vol 448-453 ◽  
pp. 1947-1950
Author(s):  
Yi Long Zhang ◽  
Yi Hui Zheng ◽  
Li Xue Li ◽  
Xin Wang ◽  
Gang Yao ◽  
...  
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Random Function ◽  
Rbf Neural Network ◽  
Partial Discharge ◽  
Rbf Network ◽  
Novel Method ◽  
Training Error ◽  
Defect Pattern ◽  
Defects Identification

With GIS being widely used, partial discharge detecting and defect pattern recognition become more and more meaningful and important. To realize defects identification of partial discharge map in GIS, a novel method based on Radical Basis Function (RBF) neural network is proposed. Firstly, a model is constructed to simulate the discharge pattern map by the use of random function randint. Secondly, based on the model above, a lot of data which meet the condition can be collected to provide for pattern recognition. Then, a RBF network is introduced to identify the pattern recognition. It can be trained by using the data above. Finally, through changing training error, high correct rate can be got. These indicate that the method is effective.

Automatic Inspection for Wafer Defect Pattern Recognition with Unsupervised Clustering

2021 ◽  
Author(s):  
Katherine Shu-Min Li ◽  
Leon Li-Yang Chen ◽  
Ken Chau-Cheung Cheng ◽  
Peter Yi-Yu Liao ◽  
Sying-Jyan Wang ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Unsupervised Clustering ◽  
Automatic Inspection ◽  
Defect Pattern
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