Abstract
Efficient power plane and stack up optimization is critical for Printed Circuit Board (PCB) Power Delivery Networks (PDNs), particularly in multi-power-domain designs with stringent DC Resistance (DCR) specifications. This work presents a novel reinforcement learning-based framework that assigns stack up layers for each power domain and iteratively refines power plane shapes to meet design constraints while ensuring non-overlapping layouts. The approach leverages Minimum Spanning Trees (MSTs) for initializing power plane shapes. It dynamically refines them using the A∗ (A-Star) algorithm with weighted pathfinding, ensuring optimal connectivity and compliance with DCR requirements. Tested extensively on multi-power-domain scenarios, the algorithm demonstrates robust performance and scalability, offering an unprecedented solution to power plane and stack up optimization challenges in PCB PDN design.
Recommended Citation
H. Manoharan et al., "Graph-Based Reinforcement Learning Approach for Multi-Power-Domain PCB PDN Shape and Stackup Synthesis," IEEE International Symposium on Electromagnetic Compatibility, pp. 303 - 308, Institute of Electrical and Electronics Engineers, Jan 2025.
The definitive version is available at https://doi.org/10.1109/EMCSIPI52291.2025.11169915
Department(s)
Electrical and Computer Engineering
Keywords and Phrases
PDN optimization; power integrity; reinforcement learning; shape synthesis; stackup synthesis
International Standard Serial Number (ISSN)
2158-1118; 1077-4076
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2025 Institute of Electrical and Electronics Engineers, All rights reserved.
Publication Date
01 Jan 2025
Comments
National Science Foundation, Grant IIP-1916535