Optimization of RRAM Read Performance and Area Efficiency: A Large-Scale and Low-Parasitic Array with Novel Interconnection Schemes

This study presents an optimization design for RRAM arrays, highlighting the trade-offs in power consumption, read speed, area efficiency, and array scalability of 1T1R arrays. To address these limitations, we propose a novel large-scale and low-parasitic array design with shared SLs across multiple rows. This design effectively reduces read power and latency while enhancing array scalability and area efficiency. Implemented in a 28nm process, it achieves an 8.6% improvement in area efficiency and a 76% enhancement in array scale compared to the conventional design, while maintaining advantages in read speed and power consumption.

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Publication Details

Published
2025-03-09
DOI
https://doi.org/10.1109/edtm61175.2025.11040239
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
0.00

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article

Optimization of RRAM Read Performance and Area Efficiency: A Large-Scale and Low-Parasitic Array with Novel Interconnection Schemes

Qishen Wang, Yuhang Yang, Zongwei Wang, Shengyu Bao et al.
Advanced Memory and Neural Computing
article

Optimization of RRAM Read Performance and Area Efficiency: A Large-Scale and Low-Parasitic Array with Novel Interconnection Schemes

Qishen Wang, Yuhang Yang, Zongwei Wang, Shengyu Bao, Linbo Shan, Yimao Cai, Ru Huang
article en

Abstract

This study presents an optimization design for RRAM arrays, highlighting the trade-offs in power consumption, read speed, area efficiency, and array scalability of 1T1R arrays. To address these limitations, we propose a novel large-scale and low-parasitic array design with shared SLs across multiple rows. This design effectively reduces read power and latency while enhancing array scalability and area efficiency. Implemented in a 28nm process, it achieves an 8.6% improvement in area efficiency and a 76% enhancement in array scale compared to the conventional design, while maintaining advantages in read speed and power consumption.

Peking University (CN)
National Science Foundation
Openalex Percentile: Top 87%
Advanced Memory and Neural Computing
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Optimization of RRAM Read Performance and Area Efficiency: A Large-Scale and Low-Parasitic Array with Novel Interconnection Schemes — Qishen Wang, Yuhang Yang, et al. · (2025) | TGRS Research Map | TGRS