few-shot learning for rare defects

**Few-Shot Learning for Rare Defects** is the **application of ML techniques that can learn to recognize new defect types from just a few (1-10) labeled examples** — critical for semiconductor manufacturing where new defect types emerge with process changes and collecting large labeled datasets is impractical. **Key Approaches** - **Metric Learning**: Learn an embedding space where similar defects cluster together (Siamese networks, prototypical networks). - **Meta-Learning**: Train a model to learn quickly from few examples (MAML, Reptile). - **Data Augmentation**: Generate synthetic variations of the few available examples. - **Foundation Models**: Use large pre-trained vision models (CLIP, DINO) as feature extractors for few-shot classification. **Why It Matters** - **New Defect Types**: Every process change can introduce novel defect types with initially very few examples. - **Fast Deployment**: Deploy a new defect classifier with just 5-10 labeled examples instead of hundreds. - **Continuous Learning**: Incrementally add new defect classes without retraining the entire model. **Few-Shot Learning** is **learning defects from a handful of examples** — enabling rapid deployment of classifiers for novel defect types with minimal labeling effort.