학술논문
A Machine Learning based Metaheuristic Technique for Decoupling Capacitor Optimization
Document Type
Conference
Source
2022 IEEE 26th Workshop on Signal and Power Integrity (SPI) Signal and Power Integrity (SPI), 2022 IEEE 26th Workshop on. :1-4 May, 2022
Subject
Language
Abstract
Decoupling capacitors are commonly used in the design and optimization of Power Delivery Networks (PDNs) in high-speed very large scale integration systems (VLSI) to minimize the variations in the power supply and to maintain a low PDN ratio. In this paper, an efficient and fast Machine Learning (ML) based surrogate-assisted metaheuristic approach is proposed for the decoupling capacitor optimization problem to reduce the cumulative impedance of the PDN below the target impedance. The performance comparison of the proposed approach with state-of-the-art approaches is also presented.