基于多元时间序列方法的忆阻器变异性和随机物理特性建模
Memristor variability and stochastic physical properties modeling from a multivariate time series approach
AI总结：
本文提出一种基于多元时间序列的建模方法，用于描述忆阻器的循环间变异性，并准确建模复位和置位电压的统计特性，实验验证于氧化铪器件。
摘要：
提出了一种强大的时间序列分析建模技术，用于描述忆阻器中的循环间变异性。这些器件表现出与器件操作固有随机性相关的变异性，并且需要精确建模以构建用于电路仿真和设计的紧凑模型。针对复位电压和置位电压提出了一种新的多元方法，该方法能准确描述电阻开关序列的统计数据结构。实验数据来自先进的氧化铪基器件。模型正确复现了实验结果，并展示了多元方法与单变量方法的比较。
英文摘要：
A powerful time series analysis modeling technique is presented to describe cycle-to-cycle variability in memristors. These devices show variability linked to the inherent stochasticity of device operation and it needs to be accurately modeled to build compact models for circuit simulation and design purposes. A new multivariate approach is proposed for the reset and set voltages that accurately describes the statistical data structure of a resistive switching series. Experimental data were measured from advanced hafnium oxide based devices. The models reproduce the experiments correctly and a comparison of the multivariate and univariate approaches is shown for comparison.
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