A Machine Learning based Metaheuristic Technique for Decoupling Capacitor Optimization

Vaghasiya, Heman; Jain, Akash; Tripathi, Jai Narayan

doi:10.1109/spi54345.2022.9874924

Cited by 2 publications

(1 citation statement)

References 14 publications

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“…This study uses a practical system earlier used in [12], [16]. It's a usual practice to select capacitors based on the anti-resonance points in the PDN impedance profile and then to place them as close to the core circuit as feasible [8].…”

Section: Problem Descriptionmentioning

confidence: 99%

Large-Scale Optimization of Decoupling Capacitors Using Adaptive Region Based Encoding Scheme in Particle Swarm Optimization

Junjariya

Tripathi

2022

IEEE Open J. Nanotechnol.

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Power delivery networks are responsible for supplying clean power to the integrated circuits. Power supply noise plays a critical role in determining the performance of high-speed very large scale integration circuits and systems. In order to maintain power integrity in high-speed systems, decoupling capacitors are used to maintain low impedance of the PDN to eventually minimize power supply noise. However, the discrete optimization problem of selecting decoupling capacitors becomes computationally challenging in the systems having stringent PI requirements. In this work, a novel approach using the Social-Learning Particle Swarm Optimization (SLPSO) technique along with Adaptive Region Search (ARS) is used to tackle the Large-Scale Optimization Problem (LSOP) of decoupling capacitor placement. Region Search (RS) is used to guide particles, followed by ARS to dynamical search for the local best positions and for particles to move faster across the search space while maintaining the diversity of the population. To demonstrate the proposed approach, three practical case studies are presented. The obtained results are compared with current state-of-the-art approaches. The proposed approach drastically reduces computation time and is consistent with better results than other approaches. This consistency of improvement in CPU time in the results of all the examples validates the proposed approach.

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Section: Problem Descriptionmentioning

confidence: 99%