Ostrovskii Valerii, Fedoseev Petr, Bobrova Yulia, Butusov Denis
Department of Computer-Aided Design, St. Petersburg Electrotechnical University "LETI", 197376 Saint Petersburg, Russia.
Department of Biomedical Engineering, St. Petersburg Electrotechnical University "LETI", 197376 Saint Petersburg, Russia.
Nanomaterials (Basel). 2021 Dec 27;12(1):63. doi: 10.3390/nano12010063.
This paper proposes a novel identification method for memristive devices using Knowm memristors as an example. The suggested identification method is presented as a generalized process for a wide range of memristive elements. An experimental setup was created to obtain a set of intrinsic I-V curves for Knowm memristors. Using the acquired measurements data and proposed identification technique, we developed a new mathematical model that considers low-current effects and cycle-to-cycle variability. The process of parametric identification for the proposed model is described. The obtained memristor model represents the switching threshold as a function of the state variables vector, making it possible to account for snapforward or snapback effects, frequency properties, and switching variability. Several tools for the visual presentation of the identification results are considered, and some limitations of the proposed model are discussed.
本文以Knowm忆阻器为例，提出了一种用于忆阻器件的新型识别方法。所建议的识别方法被呈现为适用于广泛忆阻元件的通用过程。创建了一个实验装置，以获取Knowm忆阻器的一组固有I-V曲线。利用获取的测量数据和所提出的识别技术，我们开发了一个考虑低电流效应和逐周期变化的新数学模型。描述了所提出模型的参数识别过程。所获得的忆阻器模型将开关阈值表示为状态变量向量的函数，从而能够考虑突前或突回效应、频率特性和开关变化性。考虑了几种用于直观呈现识别结果的工具，并讨论了所提出模型的一些局限性。
