Integrated circuit manufacturing is highly complex, comprising hundreds of process steps. Defects can arise at any stage, causing yield loss and ultimately degrading product reliability. Supervised methods require extensive human annotation and struggle with emergent categories and rare, data scarce defects. Clustering-based unsupervised methods often exhibit unstable performance due to missing priors. We propose IC DefectNCD, a support set free framework that leverages Image Intrinsic Priors in IC SEM images for defect detection and novel class discovery. We first develop Self Normal Information Guided IC Defect Detection, aggregating representative normal features via a learnable normal information extractor and using reconstruction residuals to coarsely localize defect regions. To handle saliency variations across defects, we introduce an adaptive binarization strategy that produces stable subimages focused on core defective areas. Finally, we design Self Defect Information Guided IC Defect Classification, which incorporates a soft mask guided attention mechanism to inject spatial defect priors into the teacher student model. This enhances sensitivity to defective regions, suppresses background interference, and enables recognition and classification of unseen defects. We validate the approach on a real world dataset spanning three key fabrication stages and covering 15 defect types. Experiments demonstrate robust performance on both defect detection and unseen defect classification.

翻译：集成电路制造过程高度复杂，包含数百道工艺步骤。缺陷可能在任何阶段产生，导致良率损失并最终降低产品可靠性。监督方法需要大量人工标注，且难以应对新出现的类别以及罕见、数据稀缺的缺陷。基于聚类的无监督方法由于缺乏先验信息，常表现出不稳定的性能。我们提出了IC DefectNCD，一种无需支持集的框架，利用集成电路扫描电镜图像中的图像内在先验进行缺陷检测和新类别发现。我们首先开发了自归一化信息引导的集成电路缺陷检测方法，通过可学习的正常信息提取器聚合代表性正常特征，并利用重建残差粗略定位缺陷区域。为处理不同缺陷间的显著性差异，我们引入了一种自适应二值化策略，生成聚焦于核心缺陷区域的稳定子图像。最后，我们设计了自缺陷信息引导的集成电路缺陷分类方法，该方法结合了软掩码引导的注意力机制，将空间缺陷先验注入师生模型中。这增强了对缺陷区域的敏感性，抑制了背景干扰，并实现了对未见缺陷的识别与分类。我们在一个涵盖三个关键制造阶段、包含15种缺陷类型的真实数据集上验证了该方法。实验表明，该方法在缺陷检测和未见缺陷分类方面均表现出鲁棒的性能。