Reinforcement Learning-based Optimal On-board Decoupling Capacitor Design Method

摘要

In this paper, for the first time, we propose a reinforcement learning-based optimal on-board decoupling capacitor (decap) design method. The proposed method can provide optimal decap designs for a given on-board power distribution network (PDN). An optimal decap design refers to the optimized combination of decaps at proper positions to satisfy a required target impedance. Moreover, a minimum number of decaps should be assigned for optimal decap designs. The proposed method is applied to the test on-board PDN and successfully provided 37 optimal decap designs with 4 decaps assigned each. Self impedance of PDN with the provided design satisfied the required target impedance while minimizing the number of assigned decaps.