An algorithm for optimal decoupling capacitor sizing and placement for standard cell layouts
Proceedings of the 2002 international symposium on Physical design
Convex Optimization
SAPOR: second-order Arnoldi method for passive order reduction of RCS circuits
Proceedings of the 2004 IEEE/ACM International conference on Computer-aided design
Relaxed hierarchical power/ground grid analysis
Proceedings of the 2005 Asia and South Pacific Design Automation Conference
Proceedings of the 2006 international symposium on Physical design
ICCAD '05 Proceedings of the 2005 IEEE/ACM International conference on Computer-aided design
Proceedings of the 43rd annual Design Automation Conference
Empire: an efficient and compact multiple-parameterized model order reduction method
Proceedings of the 2007 international symposium on Physical design
Thermal via allocation for 3-D ICs considering temporally and spatially variant thermal power
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
PRIMA: passive reduced-order interconnect macromodeling algorithm
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
System-in-Package: Electrical and Layout Perspectives
Foundations and Trends in Electronic Design Automation
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Parameterized model-order reduction is useful for very large-scale integration VLSI physical design and optimization. In this paper, we propose an efficient yet accurate parameterized model-order reduction method EMPIRE for multiple parameters. It uses implicit moment matching to efficiently handle high-order moments of a large number of parameters. In addition, it can match the moments of different parameters with different accuracy according to their influence on the objective under study, and such influence is measured by the 2-norm of their coefficient matrix in the canonical form. It develops three algorithms to further suppress the size of the reduced model by finding a projection matrix that has a much smaller number of columns than the original one. Experimental results show that compared with the best existing algorithm CORE that uses explicit moment matching for the parameters, EMPIRE reduces waveform error by 47.8 × at a similar runtime.