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忆阻器模型参数提取方法的研究与开发

Research and Development of Parameter Extraction Approaches for Memristor Models.

作者信息

Zhevnenko Dmitry Alexeevich, Meshchaninov Fedor Pavlovich, Kozhevnikov Vladislav Sergeevich, Shamin Evgeniy Sergeevich, Telminov Oleg Alexandrovich, Gornev Evgeniy Sergeevich

机构信息

Moscow Institute of Physics and Technology, 9 Institutskiy per., Dolgoprudny, 141701 Moscow, Russia.

Joint-Stock Company "Molecular Electronics Research Institute" (JSC MERI), 12/1 1st Zapadnyi Proezd, Zelenograd, 124460 Moscow, Russia.

出版信息

Micromachines (Basel). 2021 Oct 6;12(10):1220. doi: 10.3390/mi12101220.

DOI:10.3390/mi12101220
PMID:34683271
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8538760/
Abstract

Memristors are among the most promising devices for building neural processors and non-volatile memory. One circuit design stage involves modeling, which includes the option of memristor models. The most common approach is the use of compact models, the accuracy of which is often determined by the accuracy of their parameter extraction from experiment results. In this paper, a review of existing extraction methods was performed and new parameter extraction algorithms for an adaptive compact model were proposed. The effectiveness of the developed methods was confirmed for the volt-ampere characteristic of a memristor with a vertical structure: TiN/HfAlO/HfO/TiN.

摘要

忆阻器是构建神经处理器和非易失性存储器最具前景的器件之一。电路设计的一个阶段涉及建模，其中包括忆阻器模型的选择。最常见的方法是使用紧凑模型，其准确性通常取决于从实验结果中提取参数的准确性。本文对现有的提取方法进行了综述，并提出了一种用于自适应紧凑模型的新参数提取算法。所开发方法的有效性在具有垂直结构TiN/HfAlO/HfO/TiN的忆阻器的伏安特性上得到了证实。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8885/8538760/4dcf11a0a687/micromachines-12-01220-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8885/8538760/7c554757d58a/micromachines-12-01220-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8885/8538760/5fe047f6c3e3/micromachines-12-01220-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8885/8538760/e28cdc0d2baa/micromachines-12-01220-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8885/8538760/b9e01776da8c/micromachines-12-01220-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8885/8538760/e75d23aa9cee/micromachines-12-01220-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8885/8538760/a1584a6fa4a9/micromachines-12-01220-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8885/8538760/0367934d08c9/micromachines-12-01220-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8885/8538760/d70044584c2b/micromachines-12-01220-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8885/8538760/4dcf11a0a687/micromachines-12-01220-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8885/8538760/7c554757d58a/micromachines-12-01220-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8885/8538760/5fe047f6c3e3/micromachines-12-01220-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8885/8538760/e28cdc0d2baa/micromachines-12-01220-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8885/8538760/b9e01776da8c/micromachines-12-01220-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8885/8538760/e75d23aa9cee/micromachines-12-01220-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8885/8538760/a1584a6fa4a9/micromachines-12-01220-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8885/8538760/0367934d08c9/micromachines-12-01220-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8885/8538760/d70044584c2b/micromachines-12-01220-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8885/8538760/4dcf11a0a687/micromachines-12-01220-g009.jpg

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引用本文的文献

1
Memristor Degradation Analysis Using Auxiliary Volt-Ampere Characteristics.基于辅助伏安特性的忆阻器退化分析
Micromachines (Basel). 2022 Oct 8;13(10):1691. doi: 10.3390/mi13101691.

本文引用的文献

1
Analytically Embedding Differential Equation Constraints into Least Squares Support Vector Machines Using the Theory of Functional Connections.利用泛函连接理论将微分方程约束解析嵌入到最小二乘支持向量机中。
Mach Learn Knowl Extr. 2019 Dec;1(4):1058-1083. doi: 10.3390/make1040060. Epub 2019 Oct 9.
2
Memristor networks for real-time neural activity analysis.忆阻器网络用于实时神经活动分析。
Nat Commun. 2020 May 15;11(1):2439. doi: 10.1038/s41467-020-16261-1.
3
Fully hardware-implemented memristor convolutional neural network.全硬件实现的忆阻器卷积神经网络。
Nature. 2020 Jan;577(7792):641-646. doi: 10.1038/s41586-020-1942-4. Epub 2020 Jan 29.
4
Understanding memristive switching via in situ characterization and device modeling.通过原位表征和器件建模来理解忆阻开关。
Nat Commun. 2019 Aug 1;10(1):3453. doi: 10.1038/s41467-019-11411-6.
5
Memristive crossbar arrays for brain-inspired computing.忆阻器交叉阵列用于脑启发计算。
Nat Mater. 2019 Apr;18(4):309-323. doi: 10.1038/s41563-019-0291-x. Epub 2019 Mar 20.
6
Experimental demonstration of a second-order memristor and its ability to biorealistically implement synaptic plasticity.实验演示了二阶忆阻器及其生物逼真地实现突触可塑性的能力。
Nano Lett. 2015 Mar 11;15(3):2203-11. doi: 10.1021/acs.nanolett.5b00697. Epub 2015 Mar 2.
7
The missing memristor found.缺失的忆阻器被找到。
Nature. 2008 May 1;453(7191):80-3. doi: 10.1038/nature06932.