KEYWORDS: Tolerancing, Lithography, Visualization, Electroluminescence, Roads, Design for manufacturing, Photomasks, Calibration, Design for manufacturability, Current controlled current source
This paper presents an approach for compressing litho hotspot pattern library that complies with general purpose
pattern matching engine (GPPME). This approach incorporates two techniques to achieve optimal pattern reduction.
The first technique excludes polygons outside the optical diameter to reduce numerical noise related to a square
ambit which artificially may affect a hotspot location. The second technique determines the common geometrical
structures between patterns and inserts adaptive edge tolerance constraints for each individual pattern. The
performance of the resulting compressed patterns is then compared to that of running the complete library of exact
matches using an optimized exact pattern matching engine (OEPME).
The results indicate that compression rates giving number of compressed patterns in the order of hundreds can
achieve better performance than running an optimized exact pattern matcher for the whole library while maintaining
the original quality of results.
In this paper we present a modular approach which combines model based verification, pattern matching and
machine learning methods in order to achieve a high accuracy over computing time ratio.
We utilize pattern recognition technique using a supervised machine learning system (as opposed to pattern
matching) to classify the patterns either as failures (hotspots) or non-failures, and we use pattern matching to detect
all the outlier misses and false detections in each of the regions (based on the calibration set), which will be added or
removed from the set of hotspots later on. Doing so allows us to do two things: Reduce the number of patterns that
need to be pattern matched since only the outliers of the machine learning system need to be considered and more
importantly it allows us to add trained predictability to new configurations that were not in the training set but that
can be interpolated from the system.
The results indicate that indeed it is possible to successfully combine Machine learning with pattern matching
methods in order to achieve better predictability of errors of previously unseen data, while being exact in the
treatment of previously observed data.
We also explore possible avenues to further speed up the computation of the layout characterization process by
inserting a global density grid, and assess the impact of model quality and aliasing under real detection conditions.
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