Improved tangent space based distance metric for accurate lithographic hotspot classification
Improved tangent space based distance metric for accurate lithographic hotspot classification
复制标题
DOI：
10.1145/2228360.2228577
复制
发表时间：
2012-06
期刊：
影响因子：
--
通讯作者：
Jing Guo;Fan Yang;S. Sinha;C. Chiang;Xuan Zeng
中科院分区：
文献类型：
--
作者：
Jing Guo;Fan Yang;S. Sinha;C. Chiang;Xuan Zeng
模式的距离度量对于热点聚类分析和分类至关重要。在本文中，我们提出了一种改进的基于切线空间的度量，用于基于模式匹配的热点聚类分析和分类。所提出的距离度量是计算机视觉中成熟的切空间方法的重要扩展。它可以处理包含多个多边形的图案，而传统的切空间方法只能处理包含单个多边形的图案。它继承了传统切空间方法的大部分优点，例如，易于计算并且能够容忍形状的微小变化或移动。与现有的基于热点模式异或的距离度量相比，改进的基于切线空间的距离度量可以在聚类分析的背景下以最多4.3倍的计算成本实现高达37.5%的精度提高。改进的基于切线空间的距离度量是用于热点聚类分析和分类的更可靠和准确的度量。更适合行业应用。
A distance metric of patterns is crucial to hotspot cluster analysis and classification. In this paper, we propose an improved tangent space based metric for pattern matching based hotspot cluster analysis and classification. The proposed distance metric is an important extension of the well-developed tangent space method in computer vision. It can handle patterns containing multiple polygons, while the traditional tangent space method can only deal with patterns with a single polygon. It inherits most of the advantages of the traditional tangent space method, e.g., it is easy to compute and is tolerant with small variations or shifts of the shapes. Compared with the existing distance metric based on XOR of hotspot patterns, the improved tangent space based distance metric can achieve up to 37.5% accuracy improvement with at most 4.3× computational cost in the context of cluster analysis. The improved tangent space based distance metric is a more reliable and accurate metric for hotspot cluster analysis and classification. It is more suitable for industry applications.