Oxide-based memristors are two-terminal devices whose resistance can be modulated by the history of applied stimulation. Memristors have been extensively studied as memory (as resistive random access memory) and synaptic devices for neuromorphic computing applications. Understanding the internal dynamics of memristors is essential for continued device optimization and large-scale implementation. However, a model that can quantitatively describe the dynamic resistive switching (RS, e.g., set/reset cycling) behavior in a self-consistent manner, starting from the initial forming process, is still missing. In this work, we present a Ta₂O₅/TaO_x device model that can reliably predict all key RS properties during forming and repeated set and reset cycles. Our model revealed that the forming process originates from electric field focusing and localized heating effects from the initial nonuniform oxygen vacancy (V_O) defect distribution. A broad range of device behaviors, including cycling of the V_O distribution during set/reset cycles, multilevel storage, and two different filament growth processes, can be quantitatively captured by the model. In particular, a bulk-type doping effect with low programming current was found to produce linear conductance changes with a large dynamic range that can be highly desirable for neuromorphic computing applications. The simulation results were also compared with experimental dc and pulse measurements in 1R and 1T1R structures and showed excellent agreements.

中文翻译：

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定量，动态TaO _x忆阻器/电阻随机存取存储器模型

基于氧化物的忆阻器是两个端子的器件，其电阻可以通过施加刺激的历史进行调节。忆阻器已作为神经形态计算应用的记忆（作为电阻性随机存取记忆）和突触设备进行了广泛的研究。了解忆阻器的内部动态特性对于持续的器件优化和大规模实施至关重要。但是，仍然缺少从初始成型过程开始以自洽的方式定量描述动态电阻切换（RS，例如，设置/重置循环）行为的模型。在这项工作中，我们提出了Ta ₂ O ₅ / TaO _x该器件模型可以可靠地预测成型期间以及重复的设置和复位周期中的所有关键RS特性。我们的模型显示，成形过程源自电场聚焦和局部热效应，这些缺陷来自初始的不均匀氧空位（V _O）缺陷分布。多种器件行为，包括V _O的循环该模型可以定量捕获设置/重置周期，多级存储以及两个不同的细丝生长过程中的分布。特别地，发现具有低编程电流的体型掺杂效应产生具有大动态范围的线性电导变化，这对于神经形态计算应用而言是非常需要的。仿真结果还与1R和1T1R结构中的实验直流和脉冲测量结果进行了比较，并显示出极好的一致性。