Modeling Interconnect Variability Using Efficient Parametric Model Order Reduction

Assessing IC manufacturing process fluctuations and their impacts on IC interconnect performance has become unavoidable for modern DSM designs. However, the construction of parametric interconnect models is often hampered by the rapid increase in computational cost and model complexity. In this pape...

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Bibliographic Details
Published in:Design, Automation and Test in Europe pp. 958 - 963
Main Authors: Li, Peng, Liu, Frank, Li, Xin, Pileggi, Lawrence T., Nassif, Sani R.
Format: Conference Proceeding
Language:English
Published: Washington, DC, USA IEEE Computer Society 07.03.2005
IEEE
Series:ACM Conferences
Subjects:
Computational efficiency
Computing methodologies > Modeling and simulation > Model development and analysis > Modeling methodologies
Computing methodologies > Symbolic and algebraic manipulation > Symbolic and algebraic algorithms > Linear algebra algorithms
Costs
Fluctuations
Hardware > Emerging technologies
Hardware > Very large scale integration design
Integrated circuit interconnections
Integrated circuit modeling
Laboratories
Manufacturing processes
Mathematics of computing > Mathematical analysis > Numerical analysis > Computations on matrices
Mathematics of computing > Probability and statistics > Statistical paradigms
Mathematics of computing > Probability and statistics > Statistical paradigms > Statistical graphics
Parametric statistics
Performance analysis
Voltage
ISBN:9780769522883, 0769522882
ISSN:1530-1591
Online Access:Get full text
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Summary:Assessing IC manufacturing process fluctuations and their impacts on IC interconnect performance has become unavoidable for modern DSM designs. However, the construction of parametric interconnect models is often hampered by the rapid increase in computational cost and model complexity. In this paper we present an efficient yet accurate parametric model order reduction algorithm for addressing the variability of IC interconnect performance. The efficiency of the approach lies in a novel combination of low-rank matrix approximation and multi-parameter moment matching. The complexity of the proposed parametric model order reduction is as low as that of a standard Krylov subspace method when applied to a nominal system. Under the projection-based framework, our algorithm also preserves the passivity of the resulting parametric models.
Bibliography:SourceType-Conference Papers & Proceedings-1
ObjectType-Conference Paper-1
content type line 25
ISBN:9780769522883
0769522882
ISSN:1530-1591
DOI:10.1109/DATE.2005.213