Summary of Wafer Map Defect Classification Using Autoencoder-based Data Augmentation and Convolutional Neural Network, by Yin-yin Bao et al.
Wafer Map Defect Classification Using Autoencoder-Based Data Augmentation and Convolutional Neural Network
by Yin-Yin Bao, Er-Chao Li, Hong-Qiang Yang, Bin-Bin Jia
First submitted to arxiv on: 17 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Image and Video Processing (eess.IV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This study proposes a novel method to accurately categorize wafer defect maps (WDMs) in semiconductor manufacturing, which is crucial for diagnosing issues and enhancing process yields. The method combines a self-encoder-based data augmentation technique with a convolutional neural network (CNN). The self-encoder enhances data diversity by introducing noise into the latent space, mitigating class imbalance and improving generalization capabilities. The augmented dataset is then used to train the CNN, enabling it to deliver precise classification of both common and rare defect patterns. Experimental results on the WM-811K dataset demonstrate that the proposed method achieves a classification accuracy of 98.56%, outperforming Random Forest, SVM, and Logistic Regression by 19%, 21%, and 27%, respectively. This approach offers a reliable solution for wafer defect detection and classification. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps solve a big problem in making semiconductors. It’s hard to figure out what’s wrong with the wafers because there’s noisy data, some defects are more common than others, and it’s tricky to understand why they fail. The researchers came up with a new way to look at this data using something called a self-encoder and a special kind of computer program called a convolutional neural network (CNN). This helps the computer learn from all kinds of wafer patterns, even rare ones. They tested their method on a big dataset and it worked really well – much better than other methods. This could help make semiconductors more reliable. |
Keywords
» Artificial intelligence » Classification » Cnn » Data augmentation » Encoder » Generalization » Latent space » Logistic regression » Neural network » Random forest
