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Distance metric for accurate lithographic hotspot classification using radial and angular functions
Distance metric for accurate lithographic hotspot classification using radial and angular functions
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机译:使用径向和角度函数进行精确光刻热点分类的距离度量
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摘要
An dual function distance metric for pattern matching based hotspot clustering is described. The dual function distance metric can handle patterns containing multiple polygons, is easy to compute, and is tolerant of small variations or shifts of the shapes. Compared with an XOR distance metric pattern clustering, the dual function distance metric can achieve up to 37.5% accuracy improvement with 2X-4X computational cost in the context of cluster analysis. The dual function distance metric is reliable and accurate for characterizing clips (e.g. hotspots), thereby making it desirable for industry applications.
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