Preparation of Memristor and Research of Resistance-Change Mechanisms

The memristor, a recently developed electronic component, has the potential to address the limitations of data processing and storage in traditional von Neumann architecture computers. It is a promising substitute for traditional CMOS components in artificial neural networks due to its distinctive low power consumption, rapid response time, and non-volatile memory characteristics. The precise nature of the resistive change mechanism of the memristor remains unclear. The majority of research to date has focused on three main mechanisms: ionic, electronic and thermal. In regard to the selection of materials, the electrode components of memristors may be composed of metals, carbon-based materials, or conductive oxides, which possess the capacity to carry the current and engage in the resistive reaction. Memristors display a multitude of prospective applications in the field of memory devices and brain-like computing systems, given their superior characteristics. For instance, organic memristors may be employed in the biomedical sector for the production of wearable devices, given their affordability, simplicity of fabrication and biocompatibility. Furthermore, inorganic and organic materials can be combined, which is anticipated to facilitate the development of memristors with enhanced performance. These developments underscore the significance of memristors in next-generation computing and storage technologies

Authors

Institutions

Publication Details

Journal
Applied and Computational Engineering
Published
2025-01-10
DOI
https://doi.org/10.54254/2755-2721/2025.20091
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

Preparation of Memristor and Research of Resistance-Change Mechanisms

Kunying Zhou
Applied and Computational Engineering
Advanced Memory and Neural Computing
article

Preparation of Memristor and Research of Resistance-Change Mechanisms

Kunying Zhou
article en

Abstract

The memristor, a recently developed electronic component, has the potential to address the limitations of data processing and storage in traditional von Neumann architecture computers. It is a promising substitute for traditional CMOS components in artificial neural networks due to its distinctive low power consumption, rapid response time, and non-volatile memory characteristics. The precise nature of the resistive change mechanism of the memristor remains unclear. The majority of research to date has focused on three main mechanisms: ionic, electronic and thermal. In regard to the selection of materials, the electrode components of memristors may be composed of metals, carbon-based materials, or conductive oxides, which possess the capacity to carry the current and engage in the resistive reaction. Memristors display a multitude of prospective applications in the field of memory devices and brain-like computing systems, given their superior characteristics. For instance, organic memristors may be employed in the biomedical sector for the production of wearable devices, given their affordability, simplicity of fabrication and biocompatibility. Furthermore, inorganic and organic materials can be combined, which is anticipated to facilitate the development of memristors with enhanced performance. These developments underscore the significance of memristors in next-generation computing and storage technologies

Applied and Computational EngineeringVol. 124(1)
Wuhan University (CN)
Affordable and clean energy
Openalex Percentile: Top 100%
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.