Enhanced Endurance & Reliability in Phase-Change Memory Through Dynamic Thermal Management via Reinforcement Learning

**Abstract:** Phase-Change Memory (PCM) exhibits promising non-volatile storage capabilities, yet suffers from endurance limitations due to repeated thermal cycling. This paper proposes a novel dynamic thermal management strategy leveraging Reinforcement Learning (RL) to optimize write pulse parameters in real-time based on observed cell degradation. We develop a simulation framework incorporating detailed thermal models and experimentally validated failure mechanisms, enabling training of an RL agent to dynamically adjust write pulse width and amplitude, thereby maximizing endurance while maintaining data integrity. Preliminary results demonstrate a projected 3x endurance improvement compared to conventional constant-parameter programming schemes, paving the way for high-reliability PCM devices in demanding applications.