This paper provides details of the implementation of a new design hotspot classification and detection system, and
presents results of using the system to detect hotspots in layouts. A large set of hotspot snippets is grouped into a small
number of clusters containing geometrically similar hotspots. A fast incremental clustering algorithm is used to perform
this task efficiently on very large datasets. Each cluster is analyzed to produce a characterization of a class of hotspots,
and a pattern matcher is used to detect hotspots in new design layouts based on the hotspot class descriptions.
This paper proposes a new design check system that works in three steps. First, hotspots such as pinching/bridging are
recognized in a product layout based on thorough process simulations. Small layout snippets centered on hotspots are
clipped from the layout and similarities between these snippets are calculated by computing their overlapping areas. This
is accomplished using an efficient, rectangle-based algorithm. The snippet overlapping areas can be weighted by a
function derived from the optical parameters of the lithography process. Second, these hotspots are clustered using a
hierarchical clustering algorithm. Finally, each cluster is analyzed in order to identify the common cause of failure for all
the hotspots in that cluster, and its representative pattern is fed to a pattern-matching tool for detecting similar hotspots
in new design layouts. Thus, the long list of hotspots is reduced to a small number of meaningful clusters and a library of
characterized hotspot types is produced. This could lead to automated hotspot corrections that exploit the similarities of
hotspots occupying the same cluster. Such an application will be the subject of a future publication.
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