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 contrastive learning to prevent target chamber feature representations from being overly biased toward the source domain, thereby preserving the intrinsic characteristics of the target chamber. Experiments were conducted using real optical emission spectroscopy data collected from multiple chambers, and various adaptation scenarios were considered based on different source chamber selection criteria to evaluate the robustness of the proposed method. Our results demonstrate that the proposed adaptation framework consistently yields improved EPD performance across all scenarios. Furthermore, scenario analysis reveals that selecting a source chamber with a data distribution similar to the target chamber enhances adaptation performance. To facilitate effective adaptation in practical manufacturing settings, we further propose a source chamber selection algorithm based on the Wasserstein distance. Ablation studies confirm that each component of the proposed framework contributes significantly to adaptation performance improvement.