Transfer Learning for Defect Detection is the strategy of using models pre-trained on large image datasets (ImageNet) and fine-tuning them for semiconductor defect classification — overcoming the limited labeled defect data problem by leveraging features learned from millions of natural images.

How Transfer Learning Works

• Pre-Trained Backbone: Start with a CNN (ResNet, EfficientNet) pre-trained on ImageNet (1.4M images).
• Feature Reuse: Low-level features (edges, textures) transfer well to defect images.
• Fine-Tuning: Replace the final classification layer and fine-tune on defect data.
• Strategies: Freeze early layers (few labeled defects) or fine-tune all layers (more labeled data).

Why It Matters

• Limited Data: Semiconductor defect datasets are small (100s-1000s of images) — too little to train deep CNNs from scratch.
• Fast Convergence: Transfer learning converges in 10-100× fewer epochs than training from scratch.
• Domain Gap: Despite the gap between natural images and SEM/optical images, transfer learning consistently improves performance.

Transfer Learning is standing on ImageNet's shoulders — reusing knowledge from millions of images to train accurate defect detectors with limited fab data.