Cai Yuwen, Yu Wei, Zhu Qiuhao, Liu Xiyuan, Guo Xiao, Liang Wenjie
Beijing National Center for Condensed Matter Physics, Beijing Key Laboratory for Nanomaterials and Nanodevices, Institute of Physics, Chinese Academy of Sciences (CAS), Beijing 100190, China.
School of Physical Sciences, University of Chinese Academy of Sciences, Beijing 100190, China.
Nanoscale. 2025 Jul 31;17(30):17758-17768. doi: 10.1039/d5nr01019e.
The development of memristors presents a transformative opportunity to revolutionize electronic devices and computing systems by enabling non-volatile memory and neuromorphic computing. Silicon oxide memristors are particularly promising due to their potential for low cost, high integration and compatibility with existing manufacturing processes. In this study, we statistically investigate the switching mechanisms of a nanoscale (sub-2 nm) silicon oxide memristor at different temperatures. As a unipolar memristor, the average set voltage (switching from a high resistive state to a low resistive state) rises with a temperature drop, while the average reset voltage (switching from a low restive state to a high state) drops slightly with the temperature drop. Standard deviation of these values increases as temperature decreases. These behaviors are analyzed based on the Weibull distribution. Statistical results suggest that the set process involves the formation of Si conducting filaments promoted by the diffusion of oxygen ions from oxygen vacancies, while the reset process involves Joule heat-driven conductive filament rupture and silicon-oxygen recombination, requiring intensified heating at higher environmental temperatures to counteract extended oxygen ion migration. Beyond general resistive switching mechanisms involving only the formation and rupture of Si conductive filaments, our insights provide a novel understanding of the stochastic mechanisms of the switching process at the atomic level, with significant implications for future neuromorphic computing applications.
忆阻器的发展为彻底改变电子设备和计算系统带来了变革性机遇，可实现非易失性存储器和神经形态计算。氧化硅忆阻器因其低成本、高集成度以及与现有制造工艺的兼容性潜力而特别具有前景。在本研究中，我们对不同温度下纳米级（小于2纳米）氧化硅忆阻器的开关机制进行了统计研究。作为单极忆阻器，平均置位电压（从高阻态切换到低阻态）随温度下降而升高，而平均复位电压（从低阻态切换到高阻态）随温度下降略有下降。这些值的标准差随温度降低而增加。基于威布尔分布对这些行为进行了分析。统计结果表明，置位过程涉及由氧离子从氧空位扩散促进的硅导电细丝的形成，而复位过程涉及焦耳热驱动的导电细丝断裂和硅 - 氧复合，这需要在较高环境温度下加强加热以抵消延长的氧离子迁移。除了仅涉及硅导电细丝形成和断裂的一般电阻开关机制外，我们的见解为开关过程在原子水平的随机机制提供了新的理解，对未来神经形态计算应用具有重要意义。
