M-WAD: Self-Supervised Multimodal Undergain Anomaly Detection for Wafer Probing

Wafer probing is a critical inspection stage in semiconductor manufacturing, where the electrical behavior of on-wafer circuits is evaluated prior to packaging. Most existing wafer anomaly detection approaches have primarily relied on image-based inspection, while recent time-series–based models such as SwinProbeFormer have focused on detecting over-gain anomalies in probing signals. In contrast, undergain anomalies have received relatively little attention due to their subtle deviation from normal probing behavior, despite their significant impact on probing efficiency and test-cell throughput. In this paper, we propose M-WAD, a multimodal wafer anomaly detection framework that jointly leverages time-series signals, data-specific visual representations, and textual prompts to effectively detect undergain anomalies during wafer probing. M-WAD introduces a domain-aware residual image generation strategy that amplifies subtle motion degradations and enables robust multimodal representation learning. Extensive experimental results on real-world wafer probing datasets demonstrate that the proposed approach achieves state-of-the-art performance in undergain anomaly detection and consistently outperforms existing methods, including models tailored for wafer probing. These results highlight the effectiveness of multimodal learning for capturing subtle probing anomalies and its potential for practical deployment in industrial wafer testing environments.