Fast detection of largest repeating layout pattern
Jingsong Chen, James P. Shiely, Evangeline F. Y. Young · 2019
Several tools in the mask data preparation flow utilize the repetitions of patterns within a layout to reduce processing time. Conventionally, this is achieved by an analysis of the design hierarchy. However, intermediate processing in the data preparation flow can distort the hierarchy. Also, some repetitions in small-grain structures may not be represented in the hierarchy. An alternative is to learn repeating patterns and their frequency by analyzing the layout as a flat data structure. This paper demonstrates a methodology for learning the largest repeating patterns in a layout without the use of hierarchy, purely by referencing the layout as a flat data structure. The experimental results show its efficacy for layouts containing hundreds of thousands of polygons, as well as its efficiency in terms of computing time and memory usage. The results indicate that it is practical to use a distributed process to directly learn the largest repeating patterns without resorting to design hierarchy, in a reasonable runtime and memory usage.