A novel hybrid resampling for semiconductor wafer defect bin classification
Author:
Affiliation:
1. Department of Industrial Engineering and Management National Taipei University of Technology Taipei Taiwan
2. School of Computing and Augmented Intelligence Arizona State University Tempe Arizona USA
Publisher
Wiley
Subject
Management Science and Operations Research,Safety, Risk, Reliability and Quality
Link
https://onlinelibrary.wiley.com/doi/pdf/10.1002/qre.3217
Reference96 articles.
1. KongY NiD.Recognition and location of mixed‐type patterns in wafer bin maps.2019 IEEE International Conference on Smart Manufacturing Industrial & Logistics Engineering;2019.
2. A Semi-Supervised and Incremental Modeling Framework for Wafer Map Classification
3. Wafer bin map recognition using a neural network approach
4. Classification of Mixed-Type Defect Patterns in Wafer Bin Maps Using Convolutional Neural Networks
5. Detection and clustering of mixed-type defect patterns in wafer bin maps
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