SPIE Advanced Lithography 2014
Conference paper

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

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Assessing 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 that builds jointly a structured overcomplete basis, representing each reference pattern, and a linear predictor of their lithographic difficulty. A pattern from a new design is detected "novel" if its reconstruction error, when coded in the learned basis, is large. This allows a fast detection of unseen clips and a fast prediction of their lithographic difficulty. We show high speedup (1000×) compared to nearest neighbor search, and very high correlation between predicted and calculated lithographic estimate values. © 2014 SPIE.