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Proceedings Paper

Fast detection of novel problematic patterns based on dictionary learning and prediction of their lithographic difficulty
Author(s): F. de Morsier; D. DeMaris; M. Gabrani; N. Casati
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Paper 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.

Paper Details

Date Published: 31 March 2014
PDF: 14 pages
Proc. SPIE 9052, Optical Microlithography XXVII, 905211 (31 March 2014); doi: 10.1117/12.2045901
Show Author Affiliations
F. de Morsier, IBM Research – Zürich (Switzerland)
D. DeMaris, IBM Research - Austin (United States)
M. Gabrani, IBM Research – Zürich (Switzerland)
N. Casati, IBM Research – Zürich (Switzerland)

Published in SPIE Proceedings Vol. 9052:
Optical Microlithography XXVII
Kafai Lai; Andreas Erdmann, Editor(s)

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