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Proceedings Paper

Clustering and pattern matching for an automatic hotspot classification and detection system
Author(s): Justin Ghan; Ning Ma; Sandipan Mishra; Costas Spanos; Kameshwar Poolla; Norma Rodriguez; Luigi Capodieci
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Paper Abstract

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.

Paper Details

Date Published: 12 March 2009
PDF: 11 pages
Proc. SPIE 7275, Design for Manufacturability through Design-Process Integration III, 727516 (12 March 2009); doi: 10.1117/12.814328
Show Author Affiliations
Justin Ghan, Univ. of California, Berkeley (United States)
Ning Ma, Univ. of California, Berkeley (United States)
Sandipan Mishra, Univ. of California, Berkeley (United States)
Costas Spanos, Univ. of California, Berkeley (United States)
Kameshwar Poolla, Univ. of California, Berkeley (United States)
Norma Rodriguez, Advanced Micro Devices (United States)
Luigi Capodieci, Advanced Micro Devices (United States)


Published in SPIE Proceedings Vol. 7275:
Design for Manufacturability through Design-Process Integration III
Vivek K. Singh; Michael L. Rieger, Editor(s)

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