Experimental Study on Double-Layer ZnO Resistive RAM (ReRAM) by Co-Optimized Sputtering and Low-Temperature ALD Processes

This work proposed a double-layer ZnO resistive random-access-memory (ReRAM) by magnetron sputtering (MS) and low temperature (70°C) atomic layer deposition (ALD). Compared to the single-layer ZnO ReRAM by MS or ALD, the proposed dual-layer ZnO ReRAM exhibits significantly enhanced resistive switching stabilities. The introduction of dopant gases in MS can increase the resistance of the high-resistance state (HRS) effectively, with higher ON/OFF ratio and lower cycle-to-cycle variations. These findings highlight the potential of ZnO film as a promising function layer for ReRAM in various applications.

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Published
2025-06-08
DOI
https://doi.org/10.23919/snw65111.2025.11097244
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
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article

Experimental Study on Double-Layer ZnO Resistive RAM (ReRAM) by Co-Optimized Sputtering and Low-Temperature ALD Processes

Pengpeng Sang, Yifan Wu, Jiezhi Chen, Di Wu et al.
1 links
Advanced Memory and Neural Computing
article

Experimental Study on Double-Layer ZnO Resistive RAM (ReRAM) by Co-Optimized Sputtering and Low-Temperature ALD Processes

Pengpeng Sang, Yifan Wu, Jiezhi Chen, Di Wu, Junyao Mei, Xuepeng Zhan, Jixuan Wu, Bo Chen, Ruidong Li, Peng Guo
article en

Abstract

This work proposed a double-layer ZnO resistive random-access-memory (ReRAM) by magnetron sputtering (MS) and low temperature (70°C) atomic layer deposition (ALD). Compared to the single-layer ZnO ReRAM by MS or ALD, the proposed dual-layer ZnO ReRAM exhibits significantly enhanced resistive switching stabilities. The introduction of dopant gases in MS can increase the resistance of the high-resistance state (HRS) effectively, with higher ON/OFF ratio and lower cycle-to-cycle variations. These findings highlight the potential of ZnO film as a promising function layer for ReRAM in various applications.

Shandong University (CN), Sinochem Group (China) (CN), Cloud Computing Center (CN)
National Natural Science Foundation of China, Natural Science Foundation of Shandong Province
Openalex Percentile: Top 83%
Advanced Memory and Neural Computing
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