Intrinsic variability observed in resistive-switching devices (cycle-to-cycle and device-to-device) is widely recognised as a major hurdle for widespread adoption of Resistive RAM technology. While physics-based models have been developed to accurately reproduce the resistive-switching behaviour, reproducing the observed variability behavior of a specific RRAM has not been studied. Without a properly fitted variability in the model, the simulation error introduced at the device-level propagates through circuit-level to system-level simulations in an unpredictable manner. In this work, we propose an algorithm to fit a certain amount of variability to an existing physics-based analytical model (Stanford-PKU model). The extent of variability exhibited by the device is fitted to the model in a manner agnostic to the cause of variability. Further, the model is modified to better reproduce the variations observed in a device. The model, fitted with variability can well reproduce cycle-to-cycle, as well as device-to-device variations. The significance of integrating variability into RRAM models is underscored using a sensing example.

中文翻译：

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使用基于物理的模型在电路仿真中结合 RRAM 的可变性行为

在电阻开关器件（周期到周期和器件到器件）中观察到的内在可变性被广泛认为是广泛采用电阻式 RAM 技术的主要障碍。虽然已经开发出基于物理的模型来准确再现电阻开关行为，但尚未研究再现观察到的特定 RRAM 的可变性行为。如果模型中没有适当拟合的可变性，在设备级引入的仿真误差会以不可预测的方式通过电路级传播到系统级仿真。在这项工作中，我们提出了一种算法来将一定量的可变性拟合到现有的基于物理的分析模型（Stanford-PKU 模型）中。设备显示的可变性程度以与可变性原因无关的方式拟合到模型中。更多，修改模型以更好地重现在设备中观察到的变化。具有可变性的模型可以很好地再现周期到周期以及设备到设备的变化。使用传感示例强调了将可变性集成到 RRAM 模型中的重要性。