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A CNN-Based Transfer Learning Method for Defect Classification in Semiconductor Manufacturing.
- Source :
- IEEE Transactions on Semiconductor Manufacturing; Nov2019, Vol. 32 Issue 4, p455-459, 5p
- Publication Year :
- 2019
-
Abstract
- In this paper, we focus on a defect analysis task that requires engineers to identify the causes of yield reduction from defect classification results. We organize the analysis work into three phases: defect classification, defect trend monitoring and detailed classification. To support the first and third engineer’s analytical work, we use a convolutional neural network based on the transfer learning method for automatic defect classification. We evaluated our proposed methods on real semiconductor fabrication data sets by performing a defect classification task using a scanning electron microscope image and thoroughly examining its performance. We concluded that the proposed method can classify defect images with high accuracy while lowering labor costs equivalent to one-third the labor required for manual inspection work. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 08946507
- Volume :
- 32
- Issue :
- 4
- Database :
- Complementary Index
- Journal :
- IEEE Transactions on Semiconductor Manufacturing
- Publication Type :
- Academic Journal
- Accession number :
- 139499778
- Full Text :
- https://doi.org/10.1109/TSM.2019.2941752