Diffuse to Detect: Generative Diffusion Models for Unsupervised IC Anomaly Detection

ArXi:2605.26468v1 Announce Type: cross Latent defect screening is challenged by extremely low failure rates, high-dimensional test data, and absence of labeled anomalies. We propose the first unsupervised anomaly detection framework incorporating a Diffusion Transformer. Raw test measurements are first compressed by an autoencoder, then reshaped into a structured token sequence enriched with sinusoidal and per-device wafer-position embeddings.