Dynamic Microchannel Network Reconfiguration for Enhanced 48-Layer HBM5 Cooling via Reinforcement Learning

**Abstract:** This paper introduces a novel approach to dynamically reconfigure microchannel networks within 48-layer High Bandwidth Memory (HBM5) modules to optimize cooling efficiency. Leveraging Reinforcement Learning (RL) and advanced thermal simulation, we develop an agent that intelligently adjusts microchannel geometries in real-time based on observed temperature distributions. This adaptive strategy, mathematically formalized and experimentally validated through high-fidelity simulation, demonstrates a 10-15% improvement in heat dissipation compared to traditional static microchannel designs, significantly enhancing the performance and lifespan of HBM5 devices while maintaining manufacturing feasibility.