Functional interface layer for a high-performance self-rectifying memristive device using hafnium-zirconia thin film
Year: 2024
Type: article
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.
Source: Results in Engineering
Institutions: Sahmyook University, Yonsei University
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,490
OpenAlex ID: W4402713016