Pattern similarity profiling using semi-supervised learning algorithm

Two-dimensional pattern matching libraries are used to define known hotspots in the design space. These libraries can then be integrated into a physical design router to search and fix such hotspots prior to the design being completed and signed off. The task of searching for similar patterns to the known hotspot involves a significant manual effort in pattern match library development. This paper demonstrates an automated and comprehensive approach to profile the available design space for similar topological patterns based on the known hotspot and automatically generate a comprehensive master pattern library to fix and address the hotspot issue. This paper presents a semi-supervised learning algorithm for developing pattern similarity metric for pattern similarity ranking and clustering.