Step-Aware Cross-View Transformer for Unsupervised Semiconductor Manufacturing Equipment Anomaly Detection

Semiconductor manufacturing requires strict process stability, as subtle equipment degradations often accumulate gradually across consecutive wafers rather than appearing within a single run. Although deep learning approaches have achieved success in modeling process dynamics, most existing methods remain impervious to process steps, operate on wafers independently, and rely on full-scale Transformer architectures that are often too computationally expensive for real-time deployment. Herein, we propose the Step-Aware Cross-View Transformer (SACVT), a framework explicitly aligned with recipe-driven process structure and wafer-to-wafer continuity. First, SACVT introduces a hierarchical step-tokenization mechanism that treats process steps as fundamental modeling units, constraining attention within steps, improving both efficiency and process interpretability. Second, it enforces cross-waf…