基于集成学习的晶圆缺陷特征模式识别

2020 IEEE International Test Conference (ITC) Pub Date : 2020-11-01 DOI:10.1109/ITC44778.2020.9325237

L. Chen, Katherine Shu-Min Li, Ken Chau-Cheung Cheng, Sying-Jyan Wang, Andrew Yi-Ann Huang, Leon Chou, Nova Cheng-Yen Tsai, Chen-Shiun Lee

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引用次数: 2

摘要

我们提出了一种基于机器学习的晶圆缺陷图精确分类方法。提出的方法被称为TestDNA- e，因为它应用了基于改进的TestDNA特征的集成学习。实验结果表明，该方法对各种缺陷类型都有较高的命中率和整体准确率。

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TestDNA-E: Wafer Defect Signature for Pattern Recognition by Ensemble Learning

We propose a machine learning based method targeted for accurate wafer defect map classification. The proposed method is referred to as TestDNA-E, as it applies ensemble learning based on improved TestDNA features. Experimental results show that the proposed method achieves high hit rate for each defect type and overall accuracy.

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来源期刊

2020 IEEE International Test Conference (ITC)

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