发表机构
Bangladesh University of Engineering and Technology (BUET)（孟加拉国工程技术大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出融合EfficientAD与DDPM的混合单类框架，在晶圆良率图开放集异常检测中达到AUROC 0.9985，显著降低误分类，并揭示固定权重融合的局限与评估指标失衡问题。
AI 中文摘要
晶圆良率图（WBM）上的空间缺陷特征可将良率损失追溯到特定的工艺故障，然而监督分类器仅能识别训练期间见过的缺陷类型，而基于单一机制构建的单类检测器往往只能捕捉局部结构偏差或全局分布违规，很少能同时兼顾两者。本研究提出一种混合单类框架，将基于补丁的师生检测器（EfficientAD）与用于部分扩散重建的去噪扩散概率模型（DDPM）相结合，并通过固定的凸组合融合它们经百分位校准的分数。仅使用WM-38K混合类型数据集中700片正常晶圆进行训练，并在18,658片保留晶圆上评估，融合检测器的AUROC达到0.9985，并将误分类数从852（DDPM）和1,412（EfficientAD）减少至618，所有两两差异在p < 0.001水平下显著。除总体准确性外，分析表明性能提升源于两个模块之间弱重叠的错误，但固定权重融合仅恢复了Oracle选择器可用修正量的40-70%。在基准测试的倒置类别平衡下，平均精度和F1趋于饱和，而Matthews相关系数和阴性预测值揭示了正常预测的不可靠性。像素级图进一步表明，强图像级可分性并不意味空间定位能力，扩散模块作为局部密度先验而非通过全局几何推理取得成功。这些发现为混合晶圆异常检测器提出了样本自适应融合和不平衡感知评估的需求。
英文摘要
Spatial defect signatures on wafer bin maps (WBMs) trace yield loss to specific process faults, yet supervised classifiers recognize only the defect types seen during training, and one-class detectors built on a single mechanism tend to capture either local structural deviations or global distributional violations, but rarely both. This work proposes a hybrid one-class framework that couples a patch-based student-teacher detector (EfficientAD) with a denoising diffusion probabilistic model (DDPM) used for partial-diffusion reconstruction, and fuses their percentile-calibrated scores through a fixed convex combination. Trained on only 700 normal wafers from the WM-38K mixed-type dataset and evaluated on 18,658 held-out wafers, the fused detector reached an AUROC of 0.9985 and reduced misclassifications from 852 (DDPM) and 1,412 (EfficientAD) to 618, with all pairwise differences significant at p < 0.001. Beyond aggregate accuracy, the analysis shows that the gain arises from weakly overlapping errors between the two modules, yet fixed-weight fusion recovers only 40-70% of the correction available to an oracle selector. Under the benchmark's inverted class balance, average precision and F1 saturate, while the Matthews correlation coefficient and negative predictive value expose unreliable normal predictions. Pixel-level maps further show that strong image-level separability does not imply spatial localization, and the diffusion module succeeds as a local density prior rather than through global geometric reasoning. These findings motivate sample-adaptive fusion and imbalance-aware evaluation of hybrid wafer anomaly detectors.
