iClaire: A Fast and General Layout Pattern Classification Algorithm With Clip Shifting and Centroid Recreation

摘要

Layout pattern classification, which groups similar layout clips into clusters, underlies a variety of design for manufacturability (DFM) applications, such as hotspot library generation, hierarchical data storage, and yield optimization speedup. The key challenges of layout pattern classification are clip representation and clip clustering. In this paper, we present a fast and general layout pattern classification algorithm considering clip shifting and centroid recreation. Our simple but general clip representation captures both topology and density; we can handle not only rigid area match or edge displacement constraints but also variant edge tolerances and don't care regions. For achieving a small cluster count, our clip clustering is guided by the natural grouping structure of layout clips. The clustering results are further improved by centroid recreation. Our experiments are conducted on 2016 CAD contest at ICCAD benchmark suite. Our results show that our algorithm outperforms the reference solution and all contest winning teams, delivering the smallest cluster count, fastest runtime, and 100% validity. Moreover, our algorithm with clip shifting and centroid recreation further reduces the cluster count effectively and efficiently. In addition to the good solution quality, the interplay between adopted data structures and our algorithm makes it fast and viable to be incorporated into practical DFM flows.