Fast Decap Allocation Algorithm For Robust On-Chip Power Delivery
ISQED '05 Proceedings of the 6th International Symposium on Quality of Electronic Design
A fast algorithm for power grid design
Proceedings of the 2005 international symposium on Physical design
Localized On-Chip Power Delivery Network Optimization via Sequence of Linear Programming
ISQED '06 Proceedings of the 7th International Symposium on Quality Electronic Design
Thermal via planning for 3-D ICs
ICCAD '05 Proceedings of the 2005 IEEE/ACM International conference on Computer-aided design
Partitioning-based decoupling capacitor budgeting via sequence of linear programming
Integration, the VLSI Journal
Vertical via design techniques for multi-layered P/G networks
Proceedings of the 2008 Asia and South Pacific Design Automation Conference
Locality-driven parallel power grid optimization
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Design of parasitic and process-variation aware nano-CMOS RF circuits: a VCO case study
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
Proceedings of the 20th symposium on Great lakes symposium on VLSI
Optimization of via distribution and stacked via in multi-layered P/G networks
Integration, the VLSI Journal
Multi-layer interdigitated power distribution networks
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
IR-drop reduction through combinational circuit partitioning
PATMOS'06 Proceedings of the 16th international conference on Integrated Circuit and System Design: power and Timing Modeling, Optimization and Simulation
Proceedings of the Conference on Design, Automation and Test in Europe
Smart non-default routing for clock power reduction
Proceedings of the 50th Annual Design Automation Conference
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This paper presents a new method of sizing the widths of the power and ground routes in integrated circuits so that the chip area required by the routes is minimized subject to electromigration and IR voltage drop constraints. The basic idea is to transform the underlying constrained nonlinear programming problem into a sequence of linear programs. Theoretically, we show (that the sequence of linear programs always converges to the optimum solution of the relaxed convex optimization problem. Experimental results demonstrate that the proposed sequence-of-linear-program method Is orders of magnitude faster than the best-known method based on conjugate gradients with constantly better solution qualities.