Content area
Abstract
Different memristive devices have different characteristic curves; how to describe and simulate various kinds of memristive devices with a unified model is still a significant work. In this work, a new memristor model is presented—DSAM, drift speed adaptive memristor model. This model is composed of a linear i–v relation function and a speed adaptive state function. A detailed analysis of model parameters’ effect is proposed. It is shown that different parameters perform different drift speed curves, which can be adjusted to describe various memristive devices. The proposed model can also adapt to various voltage inputs. Finally, the model is tested in fitting different memristor devices with an average error of less than
Details
Title
Drift speed adaptive memristor model
Author
Li, Ya 1
; Xie, Lijun 2 ; Xiao, Pingdan 3 ; Zheng, Ciyan 4 ; Hong, Qinghui 5
1 Guangdong Polytechnic Normal University, School of Electronics and Information, Guangzhou, China (GRID:grid.410577.0) (ISNI:0000 0004 1790 2692); Guangdong Polytechnic Normal University, Guangdong Provincial Key Laboratory of Intellectual Property & Big Data, Guangzhou, China (GRID:grid.410577.0) (ISNI:0000 0004 1790 2692)
2 Guangdong Polytechnic Normal University, School of Electronics and Information, Guangzhou, China (GRID:grid.410577.0) (ISNI:0000 0004 1790 2692)
3 Hunan University, School of Physics and Electronics, Changsha, China (GRID:grid.67293.39)
4 Guangdong Polytechnic Normal University, School of Automation, Guangzhou, China (GRID:grid.410577.0) (ISNI:0000 0004 1790 2692)
5 Hunan University, College of Computer Science and Electronic Engineering, Changsha, China (GRID:grid.67293.39)
; Xie, Lijun 2 ; Xiao, Pingdan 3 ; Zheng, Ciyan 4 ; Hong, Qinghui 5
1 Guangdong Polytechnic Normal University, School of Electronics and Information, Guangzhou, China (GRID:grid.410577.0) (ISNI:0000 0004 1790 2692); Guangdong Polytechnic Normal University, Guangdong Provincial Key Laboratory of Intellectual Property & Big Data, Guangzhou, China (GRID:grid.410577.0) (ISNI:0000 0004 1790 2692)
2 Guangdong Polytechnic Normal University, School of Electronics and Information, Guangzhou, China (GRID:grid.410577.0) (ISNI:0000 0004 1790 2692)
3 Hunan University, School of Physics and Electronics, Changsha, China (GRID:grid.67293.39)
4 Guangdong Polytechnic Normal University, School of Automation, Guangzhou, China (GRID:grid.410577.0) (ISNI:0000 0004 1790 2692)
5 Hunan University, College of Computer Science and Electronic Engineering, Changsha, China (GRID:grid.67293.39)
Pages
14419-14430
Publication year
2023
Publication date
Jul 2023
ISSN
09410643
e-ISSN
14333058
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2818533354
Copyright
© The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.