Deep Reinforcement Learning-Based Pin Map Optimization of BGA Package for EMC

The radiation from the solder balls in the Ball Grid Array (BGA) packaging has become one of the main sources of electromagnetic leaks, and the pin mapping significantly impacts the package's electromagnetic compatibility (EMC). In this paper, a deep reinforcement learning (DRL)-based pin distribution optimization is proposed. Combined with the VBGT-Matrix fast calculation method, it can learn the radiation characteristics of various environments through self-training and then produce targeted pin map solutions. The outcome demonstrates that the optimization algorithm has fast speed and low power consumption, verifying the design expertise of the present industry and providing additional design insights.