Fast detection of novel problematic patterns based on dictionary learning and prediction of their lithographic difficulty

de Morsier, FrankNathalie, CasatiDeMaris, DavidGabrani, MariaGotovos, A.Krause, Andreas2013-10-252013-10-252013-10-25201410.1117/12.2045901https://infoscience.epfl.ch/handle/20.500.14299/96425Assessing pattern printability in new large layouts faces important challenges of runtime and false detection. Lithographic simulation tools and classification techniques do not scale well. We propose a fast pattern detection method by learning an overcomplete basis representing each reference pattern. A pattern from a new design is detected “novel” if its reconstruction error, when coded in the learned basis, is large. We show high speedup (1000x) compared to nearest neighbor search. A new boundary detection technique selects the minimal set of the novel patterns to predict problematic patterns; 14.93% of the novel patterns suffice to predict ORC hotspots, while 53.77% are needed using traditional methods.Large scale pattern selection and samplinghotspotsfailure regionsboundary detectionlithographic difficultyLTS5Fast detection of novel problematic patterns based on dictionary learning and prediction of their lithographic difficultytext::conference output::conference proceedings::conference paper