Functional interface layer for a high-performance self-rectifying memristive device using hafnium-zirconia thin film

With the accelerated development of artificial intelligence-oriented hardware components, research on low-power, high-density memory devices is actively being conducted. Among various memory devices, resistive switching devices with crossbar structures have been extensively researched owing to their many advantages. To address the sneak current issue that is inherent in memory devices with crossbar structures, additional selection devices have been considered. However, self-rectifying resistive switching devices are known to be advantageous for harnessing structural benefits. Although significant research has been conducted in this area and remarkable results have been published, further research is required to improve the electrical characteristics for low-power, high-density memory applications This paper introduces self-rectifying devices with low power consumption, high rectification ratios, and high reliability. By combining HfZrO x resistance-change layers and SiO x interlayers, the characteristics of self-rectifying devices were confirmed, achieving a rectification ratio of 10 6 and 100 % operational yield in 1 kb crossbar devices. The essential multiply-and-accumulate operations in artificial intelligence-oriented hardware components were verified, and the applicability of the device as an artificial neural network was explored through simulations. • High rectification ratio and low power consumption achieved with reliable operation. • Superior electrical characteristics: 100 % operational yield in 1 kb crossbar structures, enhanced retention, and endurance. • Innovative tri-layer structure of SiO x /HfZrO x /SiO x ensures robust retention and high selectivity in self-rectifying devices. • Neuromorphic computing potential verified through MAC operations and neural network simulations. • Device reliability and accuracy tested in a 32 × 32 crossbar array, impacting neuromorphic applications.

Authors

Institutions

Publication Details

Journal
Results in Engineering
Published
2024-09-16
DOI
https://doi.org/10.1016/j.rineng.2024.102906
Citations
5
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
1.04
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Functional interface layer for a high-performance self-rectifying memristive device using hafnium-zirconia thin film

June Hyuk Lee, Min Kyu Yang, Hyun Kyu Seo, Jae‐Seung Jeong et al.
5 citations
Results in Engineering
Advanced Memory and Neural Computing
1.04
article

Functional interface layer for a high-performance self-rectifying memristive device using hafnium-zirconia thin film

June Hyuk Lee, Min Kyu Yang, Hyun Kyu Seo, Jae‐Seung Jeong, Jaeho Jung, Se Yeon Jeong, Gun Hwan Kim
article en
5 citations

Abstract

With the accelerated development of artificial intelligence-oriented hardware components, research on low-power, high-density memory devices is actively being conducted. Among various memory devices, resistive switching devices with crossbar structures have been extensively researched owing to their many advantages. To address the sneak current issue that is inherent in memory devices with crossbar structures, additional selection devices have been considered. However, self-rectifying resistive switching devices are known to be advantageous for harnessing structural benefits. Although significant research has been conducted in this area and remarkable results have been published, further research is required to improve the electrical characteristics for low-power, high-density memory applications This paper introduces self-rectifying devices with low power consumption, high rectification ratios, and high reliability. By combining HfZrO x resistance-change layers and SiO x interlayers, the characteristics of self-rectifying devices were confirmed, achieving a rectification ratio of 10 6 and 100 % operational yield in 1 kb crossbar devices. The essential multiply-and-accumulate operations in artificial intelligence-oriented hardware components were verified, and the applicability of the device as an artificial neural network was explored through simulations. • High rectification ratio and low power consumption achieved with reliable operation. • Superior electrical characteristics: 100 % operational yield in 1 kb crossbar structures, enhanced retention, and endurance. • Innovative tri-layer structure of SiO x /HfZrO x /SiO x ensures robust retention and high selectivity in self-rectifying devices. • Neuromorphic computing potential verified through MAC operations and neural network simulations. • Device reliability and accuracy tested in a 32 × 32 crossbar array, impacting neuromorphic applications.

Results in EngineeringVol. 24
Yonsei University (KR), Sahmyook University (KR), University Health System (US), Yonsei University Health System (KR)
Openalex Percentile: Top 23%
Advanced Memory and Neural Computing
1.04
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.