形态学感知的模糊性学习用于晶圆缺陷决策支持
Morphology-Aware Ambiguity Learning for Wafer Defect Decision Support
- Korea University（高丽大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
提出形态学感知的模糊性学习框架，通过类别级模糊性矩阵支持单类诊断、双类辅助诊断和全面审查，在WM-811K上提升缺陷识别与决策支持效果。
AI中文摘要：
晶圆图缺陷识别通常被建模为一个固定类别的分类问题，即将每个晶圆分配给单一缺陷类别。然而，一些晶圆表现出接近类别边界的形态，对于这些晶圆，强制进行单一预测可能不如提供合理的诊断替代方案更具信息量。本文提出了一种形态学感知的模糊性学习框架，支持三种诊断操作：自动单类别诊断、具有两个合理缺陷类别的辅助诊断以及全面审查。利用训练晶圆图的径向、角度和几何特征，该框架构建了一个类别级模糊性矩阵，表示具有相似形态和合理诊断替代方案的缺陷类别对。它引导模型学习合理的替代类别，而不是将所有不正确的类别同等对待。在推理过程中，该矩阵确定不确定的预测是否可以用有意义的双类别诊断集来表示，或者是否应升级为全面审查。在WM-811K上的实验表明，所提出的框架在缺陷识别和诊断决策支持方面优于传统方法，提供有意义的双类别替代方案，同时将全面审查保留给存在未解决模糊性的情况。说明性成本分析进一步显示了所提出路由策略的潜在成本优势。该框架的诊断行为在不同骨干架构上保持一致。
英文摘要：
Wafer map defect recognition is commonly formulated as a fixed-taxonomy classification problem that assigns each wafer to a single defect class. However, some wafers exhibit morphologies near class boundaries, for which forcing a single prediction may be less informative than providing plausible diagnostic alternatives. This paper proposes a morphology-aware ambiguity learning framework that supports three diagnostic actions: automatic single-class diagnosis, assisted diagnosis with two plausible defect classes, and full review. Using the radial, angular, and geometric characteristics of training wafer maps, the framework constructs a class-level ambiguity matrix representing defect-class pairs with similar morphology and plausible diagnostic alternatives. It guides the model to learn plausible alternative classes rather than treating all incorrect classes equally. During inference, the matrix determines whether an uncertain prediction can be represented by a meaningful two-class diagnostic set or should be escalated for full review. Experiments on WM-811K show that the proposed framework outperforms conventional approaches in defect recognition and diagnostic decision support, providing meaningful two-class alternatives while reserving full review for cases with unresolved ambiguity. Illustrative cost analyses further show the potential cost advantage of the proposed routing strategy. The diagnostic behavior of the framework remains consistent across different backbone architectures.
