STT-MRAM器件自热效应的研究进展与应用挑战

Research progress and challenges of the self-heating effect in STT-MRAM devices

  • 摘要: 对自旋转移矩磁随机存取存储器(spin-transfer torque magnetic random-access memory, STT-MRAM)器件中自热效应(self-heating effect, SHE)的研究进展及面临的挑战进行系统综述。STT-MRAM作为新一代非易失性存储技术的重要候选,具备高速读写、超高集成密度、低功耗和优异耐久性等优势,在存算一体与神经形态计算等领域展现出广阔的应用前景。然而,随着器件特征尺寸的持续缩小以及对更高存储密度日益迫切的需求,写入过程中所需的高电流密度不可避免地会引发焦耳热,导致器件内部尤其是磁隧道结(magnetic tunnel junction, MTJ)局部温度的瞬时升高。这种温度的急剧变化不仅影响磁性层的热稳定性,还可能加速界面缺陷生成与材料退化,进而威胁器件的长期可靠性。研究表明,自热效应会降低器件耐久性,并显著提高写入错误率,其作用机制常呈现非线性和随机性特征。系统梳理近年来STT-MRAM自热效应方面的研究成果,深入分析其对器件耐久性与写入错误率的影响机制,并探讨其在存算一体架构应用中的核心挑战与未来的研究方向。

     

    Abstract: This paper present a systematic review of recent research progress and outstanding challenges related to the self-heating effect (SHE) in spin-transfer torque magnetic random-access memory (STT-MRAM) devices. As a leading candidate for next-generation non-volatile memory, STT-MRAM combines high-speed read/write capabilities, ultra-high integration density, low power consumption, and exceptional endurance, making it highly promising for applications in computing-in-memory, neuromorphic computing, and other advanced computing paradigms. However, with the scaling down of device dimensions and the demand for the higher storage density increases, the high current densities required during writing inevitably lead to Joule heating, causing transient local temperature elevations — particularly within the magnetic tunnel junction (MTJ). These rapid thermal fluctuations not only affect the thermal stability of the magnetic layers but also promote the formation of interfacial defects and accelerate material degradation, thereby compromising long-term reliability. Extensive studies have demonstrated that SHE degrades device endurance and markedly increases the write error rate (WER), with underlying mechanisms often exhibiting nonlinear and stochastic behavior. This review synthesizes recent advances in understanding SHE in STT-MRAM, provides an in-depth analysis of its impact on endurance and WER, and outlines key challenges and future research directions, particularly in the context of emerging computing-in-memory architectures.

     

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