SupConWI-RL: Wafer Inspection with Reinforcement Learning Enhanced by Supervised Contrastive Learning

Abstract

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 mea-surement costs. Therefore, new advanced techniques are required to reduce the cost of inspection. In this work, we ad-vocate 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-811 k and MixedWM38 datasets, showing a significant reduction in scanning time compared to CNN-based approaches while maintaining similar accu-racy. We demonstrate the reduction of up to 40% in costs as-sociated 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.