600 K temperature robustness and ten resistance levels memristor based on aluminum nitride films

The deployment of artificial intelligence hardware in extreme environments presents substantial technological challenges, particularly regarding multi-level data storage and operational stability. This work focuses on highly crystalline aluminum nitride (AlN) as memristive material with a large bandgap that is reported to exhibit outstanding thermal stability. Utilizing plasma-enhanced atomic layer deposition (PEALD), we have produced AlN films with low impurity concentrations through controlled plasma exposure duration, which permits the fabrication on Al/AlN/W memristor devices. The resulting memristor devices exhibit a pronounced dependence of electrical characteristics on film quality, demonstrating remarkable performance metrics including: (1) an on/off ratio surpassing 10 7 , (2) programmable 10-level resistance modulation, and (3) robust switching stability across an extensive temperature spectrum (@298-600 K) and retention time of >10 3 s (@600K). These advancements establish a viable framework for engineering high-performance, thermally robust memristors while contributing critical technical foundations for AI implementation under harsh operational conditions.

Authors

Institutions

Publication Details

Journal
Chip
Published
2025-11-06
DOI
https://doi.org/10.1016/j.chip.2025.100173
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

600 K temperature robustness and ten resistance levels memristor based on aluminum nitride films

Chenjia Xu, Ying Yang, Ping Zhu, Xiaoqin Yang et al.
Chip
Advanced Memory and Neural Computing
article

600 K temperature robustness and ten resistance levels memristor based on aluminum nitride films

Chenjia Xu, Ying Yang, Ping Zhu, Xiaoqin Yang, Hong Gu, Jin Cai, Lili Ma, Zengli Huang, Peng Zhu
article en

Abstract

The deployment of artificial intelligence hardware in extreme environments presents substantial technological challenges, particularly regarding multi-level data storage and operational stability. This work focuses on highly crystalline aluminum nitride (AlN) as memristive material with a large bandgap that is reported to exhibit outstanding thermal stability. Utilizing plasma-enhanced atomic layer deposition (PEALD), we have produced AlN films with low impurity concentrations through controlled plasma exposure duration, which permits the fabrication on Al/AlN/W memristor devices. The resulting memristor devices exhibit a pronounced dependence of electrical characteristics on film quality, demonstrating remarkable performance metrics including: (1) an on/off ratio surpassing 10 7 , (2) programmable 10-level resistance modulation, and (3) robust switching stability across an extensive temperature spectrum (@298-600 K) and retention time of >10 3 s (@600K). These advancements establish a viable framework for engineering high-performance, thermally robust memristors while contributing critical technical foundations for AI implementation under harsh operational conditions.

ChipVol. 5(2)
Suzhou Research Institute (CN)
Openalex Percentile: Top 67%
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.