Investigation on Temperature-Dependent Resistance States of 40nm MLC-RRAM Macro

Resistive Random-Access Memory (RRAM) is notable for its multi-level cell (MLC) capabilities, not only boosting the storage density but also enhancing efficiency in computing-in-memory (CIM) architectures. However, its conductance is highly susceptible to temperature fluctuations, challenging practical deployment. Here, we characterize the temperature-dependent resistance states of 40nm RRAM macro, indicating that conductance states have different relations with the temperature. Considering the Poole-Frenkel emission and nanowire transport, a physics model is developed to describe the resistance varying with temperature. Further, we propose the calibration method to reduce the readout and Matrix-Vector Multiplication (MVM) computation errors (5x to 25x), which is crucial for RRAM-based storage and CIM.

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Publication Details

Published
2025-03-30
DOI
https://doi.org/10.1109/irps48204.2025.10983332
Citations
2
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
1.46
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article

Investigation on Temperature-Dependent Resistance States of 40nm MLC-RRAM Macro

Shichao Zhong, Lei Sun, Linbo Shan, Lifeng Liu et al.
2 citations
Advanced Memory and Neural Computing
1.46
article

Investigation on Temperature-Dependent Resistance States of 40nm MLC-RRAM Macro

Shichao Zhong, Lei Sun, Linbo Shan, Lifeng Liu, Peng Huang, Yulin Feng, Jinfeng Kang, Hongming Guan, Yimao Cai, Zongwei Wang, K. Tao
article en
2 citations

Abstract

Resistive Random-Access Memory (RRAM) is notable for its multi-level cell (MLC) capabilities, not only boosting the storage density but also enhancing efficiency in computing-in-memory (CIM) architectures. However, its conductance is highly susceptible to temperature fluctuations, challenging practical deployment. Here, we characterize the temperature-dependent resistance states of 40nm RRAM macro, indicating that conductance states have different relations with the temperature. Considering the Poole-Frenkel emission and nanowire transport, a physics model is developed to describe the resistance varying with temperature. Further, we propose the calibration method to reduce the readout and Matrix-Vector Multiplication (MVM) computation errors (5x to 25x), which is crucial for RRAM-based storage and CIM.

Peking University (CN), Beijing Information Science & Technology University (CN)
Openalex Percentile: Top 18%
Advanced Memory and Neural Computing
1.46
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