Computer-Aided Analysis of Electronic Circuits: Algorithms and Computational Techniques
Computer-Aided Analysis of Electronic Circuits: Algorithms and Computational Techniques
On-chip decoupling capacitor optimization using architectural level prediction
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
On-chip power supply network optimization using multigrid-based technique
Proceedings of the 40th annual Design Automation Conference
A fast decoupling capacitor budgeting algorithm for robust on-chip power delivery
Proceedings of the 2004 Asia and South Pacific Design Automation Conference
Localized On-Chip Power Delivery Network Optimization via Sequence of Linear Programming
ISQED '06 Proceedings of the 7th International Symposium on Quality Electronic Design
A fast on-chip decoupling capacitance budgeting algorithm using macromodeling and linear programming
Proceedings of the 43rd annual Design Automation Conference
Optimal decoupling capacitor sizing and placement for standard-cell layout designs
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Analysis and optimization of structured power/ground networks
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Partitioning-Based Approach to Fast On-Chip Decoupling Capacitor Budgeting and Minimization
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Worst-case performance prediction under supply voltage and temperature variation
Proceedings of the 12th ACM/IEEE international workshop on System level interconnect prediction
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In this paper, we propose an efficient approach to minimize the noise on power networks via the allocation of decoupling capacitors (decap) and controlled equivalent series resistors (ESR). The controlled-ESR is introduced to reduce the on-chip power voltage fluctuation, including both voltage drop and overshoot. We formulate an optimization problem of noise minimization with the constraint of decap budget. A revised sensitivity calculation method is derived to consider both voltage drop and overshoot. The sequential quadratic programming (SQP) algorithm is adopted to solve the optimization problem where the revised sensitivity is regarded as the gradient. Experimental results show that considering voltage drop without overshoot leads to underestimating noise by 4.8%. We also demonstrate that the controlled-ESR is able to reduce the noise by 25% with the same decap budget.