semantic segmentation of defects

**Semantic Segmentation of Defects** is the **pixel-level classification of every pixel in a wafer or device image into defect categories** — assigning each pixel a label (scratch, particle, void, pattern defect, background) to create a complete defect map of the image. **Key Architectures** - **U-Net**: Encoder-decoder with skip connections — the workhorse for defect segmentation due to strong performance with limited data. - **DeepLab v3+**: Atrous spatial pyramid pooling for multi-scale feature extraction. - **SegFormer**: Transformer-based segmentation for capturing long-range spatial context. - **PSPNet**: Pyramid pooling module aggregates context at different scales. **Why It Matters** - **Complete Map**: Every pixel is classified — no defects are missed (unlike object detection bounding boxes). - **Precise Area**: Exact defect area calculation for severity assessment and yield impact analysis. - **Multiple Classes**: Simultaneously segments multiple defect types in a single forward pass. **Semantic Segmentation** is **painting every pixel with its identity** — creating complete, pixel-perfect defect maps for thorough wafer and device characterization.