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Achieving lithographic printability at advanced nodes (14nm and beyond) can impose significant restrictions on physical design, including large numbers of complex design rule checks (DRC) and compute-intensive detailed process model checking. Early identifying of yield-limiter hotspots is essential for both foundries and designers to significantly improve process maturity. A real challenge is to scan the design space to identify hotspots, and decide the proper course of action regarding each hotspot. Building a scored pattern library with real candidates for hotspots for both foundries and designers is of great value. Foundries are looking for the most used patterns to optimize their technology for and identify patterns that should be forbidden, while designers are looking for the patterns that are sensitive to their neighboring context to perform lithographic simulation with their context to decide if they are hotspots or not.[1] In this paper we propose a framework to data mine designs to obtain set of representative patterns of each design, our aim is to sample the designs at locations that can be potential yield limiting. Though our aim is to keep the total number of patterns as small as possible to limit the complexity, still the designer is free to generate layouts results in several million of patterns that define the whole design space. In order to handle the large number of patterns that represent the design building block constructs, we need to prioritize the patterns according to their importance. The proposed pattern classification methodology depends on giving scores to each pattern according to the severity of hotspots they cause, the probability of their presence in the design and the likelihood of causing a hotspot. The paper also shows how the scoring scheme helps foundries to optimize their master pattern libraries and priorities their efforts in 14nm technology and beyond. Moreover, the paper demonstrates how the hotspot scoring helps in improving the runtime of lithographic simulation verification by identifying which patterns need to be optimized to correctly describe candidate hotspots, so that only potential problematic patterns are simulated.
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