SupConWI-RL: wafer inspection with reinforcement learning enhanced by supervised contrastive learning

Aleksandr Dekhovich, Oleg Soloviev; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops, 2025, pp. 1396-1405

Monitoring manufacturing processes plays an important role in chip production. Current state-of-the-art approaches use the entire surface to classify defects with CNN- or Transformer-based models, resulting in considerable measurement costs. Therefore, new advanced techniques are required to reduce the cost of inspection. In this work, we advocate for the reinforcement learning-based feedback loop with a classifier trained with supervised contrastive loss. In contrast to previous works in this manner, our approach is not limited to only one type of defect but can identify multiple defects on one wafer. We tested our algorithm on the publicly available WM-811k and MixedWM38 datasets, showing a significant reduction in scanning time compared to CNN-based approaches while maintaining similar accuracy. We demonstrate the reduction of up to 40% in costs associated with wafer scanning in defect classification tasks, even if multiple defects are on the surface. Moreover, we demonstrate that in the multi-defect scenario, the trained model can be directly used to detect outliers, requiring only about 12.5% of the surface to find at least one type of defect.