Enhancing Wafer Probing with SwinProbeFormer: Self-supervised Anomaly Detection via Window-Based Swin Transformer

Abstract

With the rapid advancement of Artificial Intelligence (AI) and computing technologies, improving semiconductor yield and detecting defective wafers before packaging has become increasingly important. However, anomaly detection during wafer probing remains challenging due to issues such as probe-to-wafer misalignment, pad scratches, and electrical noise from friction. To address these challenges, we introduce SwinProbeFormer, a model specifically designed to detect overgain anomalies in wafer prober equipment using dynamic probe sensor data collected during the wafer probing process. Built on the Swin Transformer architecture, SwinProbeFormer leverages window-based attention and cyclic shifts to enhance spatial-temporal learning while maintaining computational efficiency. Experimental results show that SwinProbeFormer outperforms the vanilla Transformer encoder used in AnomalyBERT, achieving the highest F1 scores among baseline models. It also demonstrates strong generalization across diverse production lots, highlighting its robustness and adaptability for practical deployment in wafer probing environments.