Deep Reinforcement Learning-based Pin Assignment Optimization of BGA Packages considering Signal Integrity with Graph Representation

Abstract

In this paper, we propose a novel deep reinforcement learning (DRL)-based pin assignment method by representing ball grid array (BGA) packages on graphs to minimize signal integrity issues. The proposed method represents the pin arrangement of BGAs in graphs to formulate the pin assignment task to a variant of the maximum independent set (MIS). Then, a state-of-the-art DRL-based MIS solver was introduced to solve our task. Unlike previous methods of BGA optimization, the proposed graph representation of pins makes it possible to assign pins of any shape. Moreover, the significant scaling performance enables us to handle BGA with high pin count. We verify that the proposed DRL-based method with graph representation is effective by comparing it with conventional meta-heuristic methods including genetic algorithm (GA).