It is crucial to detect and classify defect patterns on wafers in semiconductor-manufacturing processes for wafer-quality management and prompt analysis of defect causes. In recent years, continuous technological innovation and advancements in semiconductor-industry processes have led to an increase in unknown defect patterns, which must be detected and classified. However, detection of unknown defect patterns is difficult due to complex reasons, such as training on non-existent defect classes, closed datasets owing to industrial security, and labeling large volumes of manufacturing data. Owing to these challenges, methods for detecting unknown defect patterns in an actual semiconductor-manufacturing environment primarily rely on qualitative indicators, such as intuition and experience of engineers. To overcome these problems, this study proposes a methodology based on open-set recognition to accurately detect unknown defect patterns. This methodology begins with two preprocessing steps: constrained mean filtering (C-mean filtering); and Radon transform to diminish noise and efficiently extract features from wafer-bin maps. This study then develops an entropy-estimation one-class support vector machine (EEOC-SVM), which accounts for the uncertainty in the one-class SVM classification results. EEOC-SVM computes entropy-uncertainty scores based on the distance between decision boundaries and samples and then reclassifies uncertain samples using a weighted sum of uncertainties for each class. This method can effectively detect unknown defect patterns. The proposed method achieves a detection performance of over 98 % for various defect classes based on experiments conducted with new defect patterns occurring in actual semiconductor-manufacturing environments. These results confirm that the proposed method is an effective tool for detecting and addressing unknown defect patterns.

中文翻译：

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基于开集识别方法增强对晶圆仓图上未知缺陷模式的检测


在半导体制造过程中，检测和分类晶圆上的缺陷模式对于晶圆质量管理和及时分析缺陷原因至关重要。近年来，半导体行业工艺的持续技术创新和进步导致未知缺陷模式的增加，必须对其进行检测和分类。然而，由于复杂的原因，例如对不存在的缺陷类别进行训练、由于工业安全而关闭的数据集以及标记大量制造数据，因此很难检测未知的缺陷模式。由于这些挑战，在实际半导体制造环境中检测未知缺陷模式的方法主要依赖于定性指标，例如工程师的直觉和经验。为了克服这些问题，本研究提出了一种基于开放集识别的方法，以准确检测未知缺陷模式。此方法从两个预处理步骤开始：约束均值过滤（C 均值过滤）;和 Radon 变换，以减少噪声并有效地从晶圆箱图中提取特征。然后，本研究开发了一种熵估计单类支持向量机 （EEOC-SVM），它解释了单类 SVM 分类结果的不确定性。EEOC-SVM 根据决策边界和样本之间的距离计算熵不确定性分数，然后使用每个类别的不确定性加权总和对不确定样本进行重新分类。这种方法可以有效地检测未知的缺陷模式。基于对实际半导体制造环境中出现的新缺陷模式进行的实验，所提出的方法对各种缺陷类别实现了超过 98% 的检测性能。 这些结果证实了所提出的方法是一种检测和解决未知缺陷模式的有效工具。