Directed Self-Assembly (DSA) Template Pattern Verification

Zigang Xiao, Yuelin Du, Haitong Tian, Martin D.F. Wong, He Yi, H.-S. Philip Wong, Hongbo Zhang

Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

28 Citations (Scopus)


Directed Self-Assembly (DSA) is a promising technique for contacts/vias patterning, where groups of contacts/vias are patterned by guiding templates. As the templates are patterned by traditional lithography, their shapes may vary due to the process variations, which will ultimately affect the contacts/vias even for the same type of template. Due to the complexity of the DSA process, rigorous process simulation is unacceptably slow for full chip verification. This paper formulate several critical problems in DSA verification, and proposes a design automation methodology that consists of a data preparation and a model learning stage. We present a novel DSA model with Point Correspondence and Segment Distance features for robust learning. Following the methodology, we propose an effective machine learning (ML) based method for DSA hotspot detection. The results of our initial experiments have already demonstrated the high-efficiency of our ML-based approach with over 85% detection accuracy. Compared to the minutes or even hours of simulation time in rigorous method, the methodology in this paper validates the research potential along this direction.
Original languageEnglish
Title of host publication51st ACM/IEEE Design Automation Conference 2014
PublisherAssociation for Computing Machinery (ACM)
Number of pages6
ISBN (Electronic)9781479930173
Publication statusPublished - Jun 2014
Event51st ACM/EDAC/IEEE Design Automation Conference, DAC 2014 - San Francisco, United States
Duration: 1 Jun 20145 Jun 2014 (Conference proceedings) (Conference proceedings)

Publication series

NameProceedings of ACM/IEEE Design Automation Conference
ISSN (Print)0738-100X


Conference51st ACM/EDAC/IEEE Design Automation Conference, DAC 2014
Country/TerritoryUnited States
CitySan Francisco
Internet address

User-Defined Keywords

  • Directed Self-Assembly
  • Machine Learning
  • Verification
  • Hotspot


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