2D Memory Enabled by Electrical Stimulation‐Induced Defect Engineering for Complicated Neuromorphic Computing

Abstract Defect engineering is extensively utilized in 2D memory devices due to its effectiveness in enhancing charge‐trapping ability. However, conventional defect modulation techniques usually introduce only single types of carrier traps and cannot reconfigure trap types and densities after device fabrication. Here, for the first time, electrical stimulation‐driven long‐range migration of Cu ions within CuInP 2 S 6 (CIPS) films is demonstrated to simultaneously introduce both electron and hole traps and enable reconfigurable modulation of interfacial defect trapping. This process is referred to as “electrical stimulation‐induced defect engineering”. By integrating these defect traps and the dual‐gate coupling effect, the memory window‐to‐scan range (MW/S.R) ratio, which reflects the device's charge trapping ability, doubled and peaked at 78.1% at V bg = ± 80 V. Additionally, the dual‐gate memory device based on the graphene/CIPS/h‐BN/WSe 2 heterostructure exhibits a maximum on/off ratio reaching 10 7 for multi‐level storage states, integrating neuromorphic computing and logic operations within a single platform. With 81 storage states and paired‐pulse facilitation ( PPF ), it achieves ≈90% accuracy in reservoir computing (RC) simulations. These results highlight the potential of electrical stimulation‐induced defect engineering for next‐generation electronics.

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Publication Details

Journal
Advanced Functional Materials
Published
2024-11-18
DOI
https://doi.org/10.1002/adfm.202416333
Citations
5
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
1.04

Funders

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article

2D Memory Enabled by Electrical Stimulation‐Induced Defect Engineering for Complicated Neuromorphic Computing

Yu Zheng, Jie Cheng, Bing Song, Anlian Pan et al.
5 citations
Advanced Functional Materials
Advanced Memory and Neural Computing
1.04
article

2D Memory Enabled by Electrical Stimulation‐Induced Defect Engineering for Complicated Neuromorphic Computing

Yu Zheng, Jie Cheng, Bing Song, Anlian Pan, Youwei Zhang, Weijia Tang, Pan Zhang, Xinyu Ouyang
article en
5 citations

Abstract

Abstract Defect engineering is extensively utilized in 2D memory devices due to its effectiveness in enhancing charge‐trapping ability. However, conventional defect modulation techniques usually introduce only single types of carrier traps and cannot reconfigure trap types and densities after device fabrication. Here, for the first time, electrical stimulation‐driven long‐range migration of Cu ions within CuInP 2 S 6 (CIPS) films is demonstrated to simultaneously introduce both electron and hole traps and enable reconfigurable modulation of interfacial defect trapping. This process is referred to as “electrical stimulation‐induced defect engineering”. By integrating these defect traps and the dual‐gate coupling effect, the memory window‐to‐scan range (MW/S.R) ratio, which reflects the device's charge trapping ability, doubled and peaked at 78.1% at V bg = ± 80 V. Additionally, the dual‐gate memory device based on the graphene/CIPS/h‐BN/WSe 2 heterostructure exhibits a maximum on/off ratio reaching 10 7 for multi‐level storage states, integrating neuromorphic computing and logic operations within a single platform. With 81 storage states and paired‐pulse facilitation ( PPF ), it achieves ≈90% accuracy in reservoir computing (RC) simulations. These results highlight the potential of electrical stimulation‐induced defect engineering for next‐generation electronics.

Advanced Functional MaterialsVol. 35(9)
Central South University (CN), Hunan University (CN), National University of Defense Technology (CN), Hunan Normal University (CN), Peking University (CN), Huazhong University of Science and Technology (CN)
National Natural Science Foundation of China, Natural Science Foundation of Hunan Province, National Key Research and Development Program of China, Fundamental Research Funds for Central Universities of the Central South University
Openalex Percentile: Top 22%
Advanced Memory and Neural Computing
1.04
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