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Efficient design-specific worst-case corner extraction for integrated circuits
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Source Annual ACM IEEE Design Automation Conference archive
Proceedings of the 46th Annual Design Automation Conference table of contents
San Francisco, California
SESSION: Analog/RF simulation and statistical modeling table of contents
Pages 386-389  
Year of Publication: 2009
ISBN:978-1-60558-497-3
Authors
Hong Zhang  Mentor Graphics Corporation, San Jose, CA
Tsung-Hao Chen  Mentor Graphics Corporation, San Jose, CA
Ming-Yuan Ting  Mentor Graphics Corporation, San Jose, CA
Xin Li  Carnegie Mellon University, Pittsburgh, PA
Sponsors
EDAC : Electronic Design Automation Consortium
SIGDA: ACM Special Interest Group on Design Automation
IEEE-CAS : Circuits & Systems
Publisher
ACM  New York, NY, USA
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ABSTRACT

While statistical analysis has been considered as an important tool for nanoscale integrated circuit design, many IC designers would like to know the design-specific worst-case corners for circuit debugging and failure diagnosis. In this paper, we propose a novel algorithm to efficiently extract the worst-case corners for nanoscale ICs. Our proposed approach mathematically formulates a quadratically constrained quadratic programming (QCQP) problem for corner extraction. Next, it applies the Lagrange duality theory to convert the non-convex QCQP problem to a convex semi-definite programming (SDP) problem that is easier to solve. Our circuit example designed in a commercial CMOS process demonstrates that the proposed SDP formulation can find the worst-case corners both efficiently and robustly, while the traditional QCQP fails to achieve global convergence.


REFERENCES

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