Decoupling Capacitor Selection Algorithm for PDN based on Deep Reinforcement Learning


Selection of decoupling capacitors (decaps) is important for power distribution network (PDN) design in terms of lowering impedance and saving cost. Good PDN designs typically mean satisfying a target impedance with as less decaps as possible. In this paper, an inductance-based method is utilized to calculate the port priority fist, and afterwards deep reinforcement learning (DRL) with deep neural network (DNN) is applied to optimize the assignment of decaps on the prioritized locations. The DRL algorithm can explore by itself without any prior physical knowledge, and the DNN is trained with the exploration experience and eventually converges to an optimum state. The proposed hybrid method was tested on a printed-circuit-board (PCB) example. After some iterations of training the DNN successfully reached to an optimum design, which turned out to be the minimum number of decaps that can satisfy the target impedance. The usage of DRL with DNN makes the algorithm promising to include more variables as input and handle more complicated cases in the future.

Meeting Name

2019 IEEE International Symposium on Electromagnetic Compatibility, Signal and Power Integrity, EMC+SIPI 2019 (2019: Jul. 22-26, New Orleans, LA)


Electrical and Computer Engineering

Research Center/Lab(s)

Electromagnetic Compatibility (EMC) Laboratory


This material is based upon work supported partially by the National Science Foundation under Grant No. IIP-1440110.

Keywords and Phrases

Decoupling Capacitor; Deep Q Learning; Deep Reinforcement Learning; Machine Learning; Power Distribution Network; Target Impedance

International Standard Book Number (ISBN)


Document Type

Article - Conference proceedings

Document Version


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© 2019 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.

Publication Date

01 Jul 2019