Advancing phase-change random access memory: materials innovation neuromorphic applications, and scalability challenges

Phase-change random access memory (PRAM) is an emerging high-speed, non-volatile memory technology that leverages the reversible phase transitions of chalcogenide materials such as Ge–Sb–Te to enable efficient data storage and retrieval. This paper provides a comprehensive analysis of PRAM’s core operational mechanisms, including rapid switching speeds, high endurance, and multilevel storage enabled through precise control of phase transitions. Recent advancements in materials engineering, such as nitrogen and carbon doping, superlattice-like structures, and thermoelectric effect integration, are explored for their contributions to improving thermal stability, endurance, and energy efficiency. Beyond traditional memory applications, PRAM’s unique ability to achieve tunable resistance states makes it a promising candidate for neuromorphic computing. The paper discusses PRAM’s role in emulating biological synaptic functions, such as synaptic weight modulation, spike-timing-dependent plasticity, and long-term potentiation/depression, enabling efficient learning and inference in artificial neural networks. The integration of PRAM into crossbar arrays and multi-memristive synapses is examined to develop scalable and energy-efficient neural network architectures for artificial intelligence applications. Key challenges, including sneak current phenomena, endurance degradation over repeated switching cycles, and power consumption in large-scale PRAM arrays, are critically analyzed. Potential solutions, such as selector devices, voltage scaling techniques, advanced thermal management strategies, and processing-in-memory architectures, are evaluated to address these issues. These advancements position PRAM as a pivotal technology for next-generation memory and neuromorphic computing, offering scalable, energy-efficient solutions for future high-performance and artificial intelligence-driven applications.