Fast detection of novel problematic patterns based on dictionary learning and boundary detection of failure regions
Details
Serval ID
serval:BIB_AF50844DE2F7
Type
Inproceedings: an article in a conference proceedings.
Collection
Publications
Institution
Title
Fast detection of novel problematic patterns based on dictionary learning and boundary detection of failure regions
Title of the conference
SPIE Advanced Lithography 2014
Address
San Jose, California, USA, February 23-27, 2014
Publication state
Published
Issued date
2014
Language
english
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 âeurooenovelâeuro˝ 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.
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 âeurooenovelâeuro˝ 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.
Keywords
Large scale pattern selection and sampling, hotspots, , failure regions, boundary detection, lithographic, difficulty, LTS5
Create date
06/01/2014 19:40
Last modification date
20/08/2019 16:18
Usage data
