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Layout pattern analysis using the Voronoi diagram of line segments

机译:使用线段的Voronoi图进行布局模式分析

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Early identification of problematic patterns in very large scale integration (VLSI) designs is of great value as the lithographic simulation tools face significant timing challenges. To reduce the processing time, such a tool selects only a fraction of possible patterns which have a probable area of failure, with the risk of missing some problematic patterns. We introduce a fast method to automatically extract patterns based on their structure and context, using the Voronoi diagram of line-segments as derived from the edges of VLSI design shapes. Designers put line segments around the problematic locations in patterns called "gauges," along which the critical distance is measured. The gauge center is the midpoint of a gauge. We first use the Voronoi diagram of VLSI shapes to identify possible problematic locations, represented as gauge centers. Then we use the derived locations to extract windows containing the problematic patterns from the design layout. The problematic locations are prioritized by the shape and proximity information of the design polygons. We perform experiments for pattern selection in a portion of a 22-nm random logic design layout. The design layout had 38,584 design polygons (consisting of 199,946 line segments) on layer Mx, and 7079 markers generated by an optical rule checker (ORC) tool. The optical rules specify requirements for printing circuits with minimum dimension. Markers are the locations of some optical rule violations in the layout. We verify our approach by comparing the coverage of our extracted patterns to the ORC-generated markers. We further derive a similarity measure between patterns and between layouts. The similarity measure helps to identify a set of representative gauges that reduces the number of patterns for analysis.
机译:由于光刻仿真工具面临着巨大的时序挑战,因此在超大规模集成电路(VLSI)设计中及早发现问题图案具有重要价值。为了减少处理时间,这种工具仅选择可能的模式中的一小部分,这些模式可能会出现故障,并可能会丢失一些有问题的模式。我们介绍一种快速的方法,该方法使用从VLSI设计形状的边缘派生的线段的Voronoi图,根据其结构和上下文自动提取图案。设计人员将线段放置在有问题的位置周围,称为“量规”的模式,沿着该线段可以测量关键距离。量规中心是量规的中点。我们首先使用VLSI形状的Voronoi图来识别可能存在问题的位置,以量规中心表示。然后,我们使用派生的位置从设计布局中提取包含问题图案的窗口。有问题的位置由设计多边形的形状和邻近信息确定优先级。我们在22纳米随机逻辑设计布局的一部分中进行模式选择的实验。设计布局在图层Mx上具有38,584个设计多边形(由199,946个线段组成),以及由光学规则检查器(ORC)工具生成的7079个标记。光学规则规定了对具有最小尺寸的印刷电路的要求。标记是布局中某些违反光学规则的位置。我们通过比较提取的模式与ORC生成的标记的覆盖率来验证我们的方法。我们进一步得出图案之间和布局之间的相似性度量。相似性度量有助于识别一组代表性量规,从而减少用于分析的模式数量。

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