Typically, changes in the conductive properties of resistive random‐access memory elements happen due to the movement of ions in an ultra‐thin dielectric layer under the influence of an electric field. In the case of oxides, they often talk about the movement of oxygen vacancies and the formation/destruction of conducting filaments. Such processes are often described by dynamic systems in which the state parameter corresponds to the position of the boundary between regions with low and high concentrations of oxygen vacancies. In this case, the dependence of the current (or resistance) on the state parameter and the voltage applied to the element can be quite complex. In this regard, the work proposes an approach that uses neural networks to approximate the dependence of the current on the state parameter and voltage. Thus, a hybrid model is obtained in which the state parameter is determined using a dynamic system that takes into account the basic physical characteristics of the elements, and the model is finely tuned to the experimental data at the neural network level. A hybrid model for a memristor based on hafnium oxide (HfO2), as well as on nanocomposite (Co–Fe–B)m(LiNbO3)100−m, has been successfully constructed.

中文翻译：

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忆阻元件建模的混合方法

通常，电阻式随机存取存储器元件的导电特性的变化是由于离子在电场影响下在超薄介电层中的移动而发生的。就氧化物而言，他们经常谈论氧空位的移动和导电丝的形成/破坏。这种过程通常由动态系统来描述，其中状态参数对应于具有低和高浓度氧空位的区域之间的边界位置。在这种情况下，电流（或电阻）对状态参数和施加到元件的电压的依赖性可能非常复杂。在这方面，该工作提出了一种使用神经网络来近似电流对状态参数和电压的依赖性的方法。因此，获得了一个混合模型，其中使用考虑了元件基本物理特性的动态系统来确定状态参数，并且该模型在神经网络级别上根据实验数据进行了微调。基于氧化铪 (HfO2），以及纳米复合材料（Co-Fe-B）米(铌酸锂3）100−米，已建设成功。