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

Authors
  • de Morsier, Frank
  • Nathalie, Casati
  • DeMaris, David
  • Gabrani, Maria
  • Gotovos, A.
  • Krause, Andreas
Publication Date
Jan 01, 2014
Source
Infoscience @ EPFL
Keywords
License
Unknown
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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 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.

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