Mortise-tenon–shaped memristors for scientific computing

In-memory computing hardware based on memristors has emerged as a promising option for scientific computing due to its large-scale parallel data processing capability. However, the nonuniformity issue of the memristors renders the practical deployment of in-memory computing hardware complex, requiring peripheral circuits to ensure the accuracy of scientific computing, thereby resulting in increased power consumption. Here, we present a mortise-tenon-shaped (MTS) memristor with ultrahigh uniformity by introducing a mortise-shaped h-BN flake on the HfO<sub>2</sub> switching layer. The MTS memristor exhibits ultrasmall cycle-to-cycle (~2.5%) and device-to-device (~6.9%) variations compared to the HfO<sub>2</sub> memristor without the MTS structure. Furthermore, we use the MTS memristors to build a partial differential equation solver and demonstrate a convergence speed of solving the Poisson equation five times faster than the solver based on the traditional HfO<sub>2</sub> memristors. This work provides a promising approach for notably reducing the hardware resources required for fast and high-accuracy scientific computing.

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Publication Details

Journal
Science Advances
Published
2025-05-01
DOI
https://doi.org/10.1126/sciadv.adu3309
Citations
15
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
10.96
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article

Mortise-tenon–shaped memristors for scientific computing

Qiuyang Tan, Fanqiang Chen, Chao Zhu, Weiqi Dang et al.
15 citations
Science Advances
Advanced Memory and Neural Computing
10.96
article

Mortise-tenon–shaped memristors for scientific computing

Qiuyang Tan, Fanqiang Chen, Chao Zhu, Weiqi Dang, Wei Wei, J. Shi, Feng Miao, Shi‐Jun Liang, Bin Cheng, Shaobo Cheng, Chen Pan, Xing-Jian Yangdong, Luo Yan, Cong Wang, Pengfei Wang, Li Zhong, Yongqin Xie, Shuang Wang, Ying Guo, Gong-Jie Ruan, Sicheng Chen, Zaizheng Yang, Yu Shen
article en
15 citations

Abstract

In-memory computing hardware based on memristors has emerged as a promising option for scientific computing due to its large-scale parallel data processing capability. However, the nonuniformity issue of the memristors renders the practical deployment of in-memory computing hardware complex, requiring peripheral circuits to ensure the accuracy of scientific computing, thereby resulting in increased power consumption. Here, we present a mortise-tenon-shaped (MTS) memristor with ultrahigh uniformity by introducing a mortise-shaped h-BN flake on the HfO<sub>2</sub> switching layer. The MTS memristor exhibits ultrasmall cycle-to-cycle (~2.5%) and device-to-device (~6.9%) variations compared to the HfO<sub>2</sub> memristor without the MTS structure. Furthermore, we use the MTS memristors to build a partial differential equation solver and demonstrate a convergence speed of solving the Poisson equation five times faster than the solver based on the traditional HfO<sub>2</sub> memristors. This work provides a promising approach for notably reducing the hardware resources required for fast and high-accuracy scientific computing.

Science AdvancesVol. 11(18)
Nanjing University of Science and Technology (CN), Zhengzhou University (CN), Collaborative Innovation Center of Advanced Microstructures (CN), Southeast University (CN)
Openalex Percentile: Top 1%
Advanced Memory and Neural Computing
10.96
DOIOpen Access PDFOpenAlex
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