Abstract
The selection of decoupling capacitors (decap) is a critical but tedious process in power distribution network (PDN) design. In this paper, an improved decap-selection algorithm based on deep reinforcement learning (DRL), which seeks the minimum number of decaps through a self-exploration training to satisfy a given target impedance, is presented. Compared with the previous relevant work: the calculation speed of PDN impedance is significantly increased by adopting an impedance matrix reduction method; also, the enhanced algorithm performs a better convergence by utilizing the techniques of double Q-learning and prioritized experience replay; furthermore, a well-designed reward is proposed to facilitate long-term convergence when more decaps are required. The proposed algorithm demonstrates the feasibility of achieving decent performance using DRL with pre-trained knowledge for more complicated engineering tasks in the future.
Recommended Citation
L. Zhang et al., "An Enhanced Deep Reinforcement Learning Algorithm for Decoupling Capacitor Selection in Power Distribution Network Design," 2020 IEEE International Symposium on Electromagnetic Compatibility and Signal/Power Integrity, EMCSI 2020, pp. 245 - 250, article no. 9191512, Institute of Electrical and Electronics Engineers, Jul 2020.
The definitive version is available at https://doi.org/10.1109/EMCSI38923.2020.9191512
Department(s)
Electrical and Computer Engineering
Keywords and Phrases
decoupling capacitor (decap); deep Q-learning; double Q-learning; machine learning; power distribution network; prioritized experience replay; reward; target impedance
International Standard Book Number (ISBN)
978-172817430-3
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Institute of Electrical and Electronics Engineers, All rights reserved.
Publication Date
01 Jul 2020
Comments
National Science Foundation, Grant 1916535