用作RRAM氧化物层的金属氧化物组合与人工智能
The Combination of Metal Oxides as Oxide Layers for RRAM and Artificial Intelligence
AI总结：
本综述梳理了基于金属氧化物的RRAM与AI的双向融合研究，涵盖AI优化RRAM性能、RRAM赋能AI的进展，指出领域挑战并展望未来方向。
摘要：
阻变随机存取存储器（Resistive random-access memory, RRAM）凭借高速、低功耗及出色的可扩展性，成为下一代存储器件的有力候选。金属氧化物因介电常数高、稳定性好，常被用作RRAM器件的氧化物层。然而，为进一步提升RRAM器件性能，近期研究聚焦于与人工智能（artificial intelligence, AI）的融合：AI可用于优化RRAM器件性能，RRAM也可作为硬件加速器并在神经形态计算中为AI提供算力支撑。本综述概述了基于金属氧化物的RRAM与AI的结合进展，重点介绍了上述两个方向的最新研究成果，讨论了AI用于提升RRAM器件性能、RRAM用于赋能AI的相关应用，同时阐述了该领域的关键挑战，并对未来研究方向提出了见解。
英文摘要：
Resistive random-access memory (RRAM) is a promising candidate for next-generation memory devices due to its high speed, low power consumption, and excellent scalability. Metal oxides are commonly used as the oxide layer in RRAM devices due to their high dielectric constant and stability. However, to further improve the performance of RRAM devices, recent research has focused on integrating artificial intelligence (AI). AI can be used to optimize the performance of RRAM devices, while RRAM can also power AI as a hardware accelerator and in neuromorphic computing. This review paper provides an overview of the combination of metal oxides-based RRAM and AI, highlighting recent advances in these two directions. We discuss the use of AI to improve the performance of RRAM devices and the use of RRAM to power AI. Additionally, we address key challenges in the field and provide insights into future research directions
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
