Adaptive learning strategies for addressing chamber variations in real-time endpoint detection of semiconductor plasma etching

Abstract As wafer open areas decrease and circuit designs become more intricate, the demand for precise endpoint detection (EPD) in etching processes has increased. However, variations among plasma chambers induce covariate shifts in data distributions, degrading the generalization capability of machine learning-based EPD models. To address this issue, we propose a contrastive entropy-conditioned chamber adaptation framework that enables robust EPD in new (target) chambers exhibiting distribution shifts from existing (source) chambers, even without labeled data from the target chamber, by leveraging adversarial learning. Our approach incorporates two additional strategies to enhance adaptation effectiveness. First, we introduce entropy conditioning that assigns larger weights to data samples exhibiting high transferability between the source and target chambers. Second, we employ contras…