QoR analysis of fractured data solutions using distributed processing

Mask shops perform the QoR analysis of the fractured data by measuring the quality of the various basic metrics like the shot count, sliver count, smashed figure count, file size, shot perimeter, sliver perimeter, number of thin slivers etc. Other than the basic metrics mentioned above, the QoR of fractured data is also judged upon more advanced quality metrics like the number of CD splits, number of embedded and shoreline slivers as well as the lengths of embedded and shoreline slivers. Computation of these advanced metrics involves complex and compute-intensive algorithms, especially because the fractured mask data sizes have already reached hundreds of GBs. Hence, an efficient distributed processing solution with fast turn-around-time is required to measure the overall QoR metrics of fractured data solutions. This paper clearly describes the definitions of various QoR metrics and then describes parallelizable schemes to measure these QoR metrics. Another important QoR metric of the fractured data is the orientation-independent fracturing uniformity. Fracturing uniformity plays a significant role in ensuring CD uniformity. This paper introduces the concept of fracturing uniformity and discusses the issues in detecting the same.