Summary of An Evaluation Of Continual Learning For Advanced Node Semiconductor Defect Inspection, by Amit Prasad et al.
An Evaluation of Continual Learning for Advanced Node Semiconductor Defect Inspection
by Amit Prasad, Bappaditya Dey, Victor Blanco, Sandip Halder
First submitted to arxiv on: 17 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 A deep learning-based approach to semiconductor defect inspection offers high accuracy, adaptability, and efficiency. However, emerging new types of defects pose a challenge for traditional detectors, which may suffer from catastrophic forgetting when trained on new datasets. To address this, the paper proposes a task-agnostic meta-learning approach that enables incremental addition of new defect classes and scales to create a more robust model. The method is benchmarked using real SEM datasets for two process steps, ADI and AEI, demonstrating superior performance compared to conventional supervised training. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers has developed a better way to inspect tiny defects in semiconductors. They used special computers to learn from lots of pictures of defects. This helped the computer understand how to spot new types of defects that might not have been seen before. The method is useful because it can be updated with new information without forgetting what it already knows. This means it can keep getting better and better at detecting defects. |
Keywords
* Artificial intelligence * Deep learning * Meta learning * Supervised
