Diffuse to Detect: Generative Diffusion Models for Unsupervised IC Anomaly Detection
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Computer Science > Machine Learning
Title:Diffuse to Detect: Generative Diffusion Models for Unsupervised IC Anomaly Detection
Abstract: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. Anomaly scores are derived from the noise-prediction error over mid-range diffusion timesteps, enabling fast wafer-scale screening without any labeled defects or manual feature engineering. Our approach achieves state-of-the-art performance on industrial 16nm IC test data under extreme class imbalance, offering interpretable failure localization through latent-space reconstruction residuals.
| Comments: | 9 pages, 5 figures |
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2605.26468 [cs.LG] |
| (or arXiv:2605.26468v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2605.26468
arXiv-issued DOI via DataCite (pending registration)
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