用生成式扩散模型实现无监督晶圆缺陷检测，无需标签也能精准定位异常。
Diffuse to Detect: Generative Diffusion Models for Unsupervised IC Anomaly Detection
- 通过自编码器压缩数据并生成带位置嵌入的令牌序列，结合扩散变换器建模。
- 在16nm芯片测试数据上达到顶尖性能，误报率极低且支持晶圆级快速筛查。
- 适合工业界做无标注缺陷检测，结果可解释性强，适合芯片制造质量控制。
晶圆缺陷检测面临缺陷率极低、测试数据维度高且无标签异常的挑战。本文提出首个融合扩散变换器的无监督异常检测框架：原始测试数据经自编码器压缩后，转化为含正弦及晶圆位置嵌入的结构化令牌序列；异常分数基于中段扩散时间步的噪声预测误差计算，实现无需标注缺陷的快速晶圆级筛查。该方法在极端类别不平衡的工业16nm IC测试数据上达到当前最优性能，并通过潜在空间重构残差提供可解释的故障定位。
原文摘要 · Abstract (English)
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.
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