Generative Models

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Generative Models

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1,168 papers

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Diffuse to Detect: Generative Diffusion Models for Unsupervised IC Anomaly Detection
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May 26, 2026 · cs.LGJ/K move · Enter open · S save

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

Yuxuan Yin, Chen He, Todd Jacobs, Jialei He+3

Department of Electrical and Computer Engineering, University of California Santa Barbara, CA, USA · Automotive Processing, NXP Semiconductors, TX, USA

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.

Detection Framework Generative

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