Nanoparticles-array-air spacer passivated memristor with high on/off ratio and low reset current density

Non-volatile resistive random-access memory (ReRAM) is a promising candidate for next-generation information storage, such as radiation-resistant memory modules and multifunctional memristor for sensing, data storage, and computing. However, ReRAM faces critical challenges in simultaneously achieving high on/off ratios and low reset current density due to conflicting material requirements that demand both high electrical conductivity and low thermal conductivity. Herein, we propose a novel nanoparticles (NPs)-array-air spacer (NAAS) passivated strategy to resolve the inherent electrical-thermal conductivity trade-off in ReRAM design. Specifically, we demonstrated an Al/PMMA/NAAS/ITO memristor featuring the highest on/off ratio (10<sup>7</sup>) and the lowest reset current density (10<sup>−9</sup> A/cm<sup>2 </sup>at 0.02 V read) reported to date. The Au NAAS, formed by monodisperse Au NPs self-assembled on ITO and interstitial air gaps, served as a passivated layer between ITO and suspended PMMA film. Both experimental characterization and electrical/thermal simulations confirm that such unique architecture strategically decouples the conflicting requirements by reducing overall thermal conductivity while enhancing local electrical conductivity, yielding simultaneously a record-high on/off ratio and ultralow reset current density. This spatial passivation strategy transcends conventional single-material approaches, providing a universal design paradigm for reconciling conflicting material requirements in nanoscale resistive switching devices.

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Publication Details

Journal
Nano Research
Published
2025-09-30
DOI
https://doi.org/10.26599/nr.2025.94908111
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
0.00

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Nanoparticles-array-air spacer passivated memristor with high on/off ratio and low reset current density

Bo Zhao, Lin Jiang, Xinpeng Wang, Oleksandr Ivasenko et al.
Nano Research
Advanced Memory and Neural Computing
article

Nanoparticles-array-air spacer passivated memristor with high on/off ratio and low reset current density

Bo Zhao, Lin Jiang, Xinpeng Wang, Oleksandr Ivasenko, Habibur Rehman, Weijing Shao, Wanyu Lu, Chang Liu, Yawen Wang, Dong Li, Yu Wang, Yinghui Sun, Yandong Wang
article en

Abstract

Non-volatile resistive random-access memory (ReRAM) is a promising candidate for next-generation information storage, such as radiation-resistant memory modules and multifunctional memristor for sensing, data storage, and computing. However, ReRAM faces critical challenges in simultaneously achieving high on/off ratios and low reset current density due to conflicting material requirements that demand both high electrical conductivity and low thermal conductivity. Herein, we propose a novel nanoparticles (NPs)-array-air spacer (NAAS) passivated strategy to resolve the inherent electrical-thermal conductivity trade-off in ReRAM design. Specifically, we demonstrated an Al/PMMA/NAAS/ITO memristor featuring the highest on/off ratio (10<sup>7</sup>) and the lowest reset current density (10<sup>−9</sup> A/cm<sup>2 </sup>at 0.02 V read) reported to date. The Au NAAS, formed by monodisperse Au NPs self-assembled on ITO and interstitial air gaps, served as a passivated layer between ITO and suspended PMMA film. Both experimental characterization and electrical/thermal simulations confirm that such unique architecture strategically decouples the conflicting requirements by reducing overall thermal conductivity while enhancing local electrical conductivity, yielding simultaneously a record-high on/off ratio and ultralow reset current density. This spatial passivation strategy transcends conventional single-material approaches, providing a universal design paradigm for reconciling conflicting material requirements in nanoscale resistive switching devices.

Nano ResearchVol. 19(3)
National Natural Science Foundation of China, Ministry of Science and Technology of the People's Republic of China, China Postdoctoral Science Foundation, Ministry of Industry and Information Technology of the People's Republic of China, Soochow University, Higher Education Discipline Innovation Project
Openalex Percentile: Top 74%
Advanced Memory and Neural Computing
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