Retention Characteristic Optimization Based on Combined Forming Scheme for Resistive Random Access Memory Chip

The long-time retention issue of resistive random access memory (RRAM) presents a significant challenge in maintaining the performance of large-scale RRAM-based computation-in-memory (CIM) systems. To address the long-term inference accuracy degradation caused by RRAM instability, we proposed a combined forming strategy, which can effectively suppress resistance drift and improve inference accuracy without periodic updates of RRAM cells. With this optimized strategy, the probability of high resistance state (HRS) drift was reduced to 5%, and the inference accuracy could be maintained at 88% even after 105s at 125°. This work provided a valuable strategy for enhancing devices retention and sustaining accuracy in RRAM-based CIM systems.

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Publication Details

Journal
IEEE Electron Device Letters
Published
2025-04-29
DOI
https://doi.org/10.1109/led.2025.3565496
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
0.00

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article

Retention Characteristic Optimization Based on Combined Forming Scheme for Resistive Random Access Memory Chip

Qingyun Zuo, Xu Zheng, Yudi Zhao, Xiaoxin Xu et al.
IEEE Electron Device Letters
Advanced Memory and Neural Computing
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Retention Characteristic Optimization Based on Combined Forming Scheme for Resistive Random Access Memory Chip

Qingyun Zuo, Xu Zheng, Yudi Zhao, Xiaoxin Xu, Wubo Li, Hao Min, Yifei Lü, Qi Liu, Yuhang Zhao, Wenchang Zhang, Qiqiao Wu, Yixuan Liu
article en

Abstract

The long-time retention issue of resistive random access memory (RRAM) presents a significant challenge in maintaining the performance of large-scale RRAM-based computation-in-memory (CIM) systems. To address the long-term inference accuracy degradation caused by RRAM instability, we proposed a combined forming strategy, which can effectively suppress resistance drift and improve inference accuracy without periodic updates of RRAM cells. With this optimized strategy, the probability of high resistance state (HRS) drift was reduced to 5%, and the inference accuracy could be maintained at 88% even after 105s at 125°. This work provided a valuable strategy for enhancing devices retention and sustaining accuracy in RRAM-based CIM systems.

IEEE Electron Device LettersVol. 46(7)
Chinese Academy of Sciences (CN), Fudan University (CN), Shanghai Innovative Research Center of Traditional Chinese Medicine (CN), Institute of Microelectronics (CN), Shanghai Fudan Microelectronics (China) (CN), Beijing Information Science & Technology University (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 93%
Advanced Memory and Neural Computing
DOISourceOpenAlex
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