论文标题
用于空间过滤的图理论方法及其对晶圆箱中混合型空间图案识别的影响
A Graph-Theoretic Approach for Spatial Filtering and Its Impact on Mixed-type Spatial Pattern Recognition in Wafer Bin Maps
论文作者
论文摘要
半导体制造中的统计质量控制取决于有效的晶圆箱地图诊断，其中关键挑战是检测有缺陷的芯片如何倾向于在晶圆上空间聚集 - 这个问题称为空间模式识别。最近，对混合型空间模式识别的兴趣越来越大 - 当多种缺陷模式（不同形状）在同一晶圆上共存时。混合型空间模式识别需要两个中心任务：（1）空间滤波，以将系统模式与随机噪声区分开； （2）空间聚类，将过滤模式分为不同的缺陷类型。观察到空间滤波对高质量的混合型模式识别有用，我们建议使用一种称为邻接聚类的图理论方法，该方法利用了相邻缺陷的芯片之间的空间依赖性，以有效地过滤原始的晶状图。根据现实世界数据进行了测试，并与最先进的方法进行了比较，我们提出的方法在内部群集验证质量（即没有外部类别标签的验证质量）方面至少获得46％的增益，而在归一化信息的情况下，基于外部类别的外部群集验证指标，基于外部类别的指标。有趣的是，改进的边缘似乎是模式复杂性的函数，对于更复杂形状的模式获得了更大的收益。
Statistical quality control in semiconductor manufacturing hinges on effective diagnostics of wafer bin maps, wherein a key challenge is to detect how defective chips tend to spatially cluster on a wafer--a problem known as spatial pattern recognition. Recently, there has been a growing interest in mixed-type spatial pattern recognition--when multiple defect patterns, of different shapes, co-exist on the same wafer. Mixed-type spatial pattern recognition entails two central tasks: (1) spatial filtering, to distinguish systematic patterns from random noises; and (2) spatial clustering, to group filtered patterns into distinct defect types. Observing that spatial filtering is instrumental to high-quality mixed-type pattern recognition, we propose to use a graph-theoretic method, called adjacency-clustering, which leverages spatial dependence among adjacent defective chips to effectively filter the raw wafer maps. Tested on real-world data and compared against a state-of the-art approach, our proposed method achieves at least 46% gain in terms of internal cluster validation quality (i.e., validation without external class labels), and about ~5% gain in terms of Normalized Mutual Information--an external cluster validation metric based on external class labels. Interestingly, the margin of improvement appears to be a function of the pattern complexity, with larger gains achieved for more complex-shaped patterns.
