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

Abstract

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.