Novel Modeling Methodology for Memristive Devices for Circuit Design and Simulation

This work introduces a methodology for obtaining the high-level model of memristive devices. The methodology uses the I- V curve points of the device. Model equations are derived from the calculation of a state-variable of a first-order differential equation that describes the dynamics of the device. As a demostration, the procedure has been applied to three classical memristor models from which new modeling equations have been developed. Comparison between the resulting model and the initial data points is carried out by measuring the Mean Absolute Percentage Error (MAPE), resulting in 4%, 2.8% and 20.1% when compared to original Strukov's, Biolek's and Chang's models, respectively. This methodology simplifies the simulation process by maintaining a first-order state variable for the model, and avoiding highly non-linear equations such as those introduced by window funcions in the port equation.

Authors

Institutions

Publication Details

Published
2024-10-23
DOI
https://doi.org/10.1109/cce62852.2024.10771006
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Novel Modeling Methodology for Memristive Devices for Circuit Design and Simulation

Arturo Sarmiento-Reyes, Jesús Jiménez-León, Pedro Rosales
Advanced Memory and Neural Computing
article

Novel Modeling Methodology for Memristive Devices for Circuit Design and Simulation

Arturo Sarmiento-Reyes, Jesús Jiménez-León, Pedro Rosales
article en

Abstract

This work introduces a methodology for obtaining the high-level model of memristive devices. The methodology uses the I- V curve points of the device. Model equations are derived from the calculation of a state-variable of a first-order differential equation that describes the dynamics of the device. As a demostration, the procedure has been applied to three classical memristor models from which new modeling equations have been developed. Comparison between the resulting model and the initial data points is carried out by measuring the Mean Absolute Percentage Error (MAPE), resulting in 4%, 2.8% and 20.1% when compared to original Strukov's, Biolek's and Chang's models, respectively. This methodology simplifies the simulation process by maintaining a first-order state variable for the model, and avoiding highly non-linear equations such as those introduced by window funcions in the port equation.

National Institute of Astrophysics, Optics and Electronics (MX)
Openalex Percentile: Top 77%
Advanced Memory and Neural Computing
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Novel Modeling Methodology for Memristive Devices for Circuit Design and Simulation — Arturo Sarmiento-Reyes, Jesús Jiménez-León, et al. · (2024) | TGRS Research Map | TGRS