Conductive dendrite engineering of single-crystalline two-dimensional dielectric memristors

Ultralow-power non-volatile memristors are key elements in electronics. Generally, power reduction of memristors compromises data retention, a challenge known as the "power-retention dilemma," due to the stochastic formation of conductive dendrites in resistive-switching materials. Here, we report the results of conductive dendrite engineering in single-crystalline two-dimensional (2D) dielectrics in which directional control of filamentary distribution is possible. We find that the single-vacancy density (n<sub>SV</sub>) of single-crystalline hexagonal boron nitride (h-BN) plays an essential role in regulating conductive dendrite growth, supported by scanning joule expansion microscopy (SJEM). With optimized n<sub>SV</sub>, random dendrite growth is largely limited, and electrons hop between the neighboring Ag nanoclusters in vertical channels. The corresponding model was established to probe the relationship between n<sub>SV</sub> and memristor operating voltage. The conductive channel confinement in the vertical orientation contributes to long-retention non-volatile memristors with ultralow switch voltages (set: 26 mV; reset: -135 mV), excellent power efficiency (4 fW standby and a switching energy of 72 pJ) while keeping a high on/off resistance ratio of 10<sup>8</sup>. Even at a record-low compliance current of 10 nA, memristors retains very robust non-volatile, multiple resistive states with an operating voltage less than 120 mV (the per-transition power low as 900 pW).

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Publication Details

Journal
The Innovation
Published
2025-03-18
DOI
https://doi.org/10.1016/j.xinn.2025.100885
Citations
8
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
5.84
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article

Conductive dendrite engineering of single-crystalline two-dimensional dielectric memristors

Yuda Zhao, Neng Wan, Xingyu Zhai, Baoshi Qiao et al.
8 citations
The Innovation
Advanced Memory and Neural Computing
5.84
article

Conductive dendrite engineering of single-crystalline two-dimensional dielectric memristors

Yuda Zhao, Neng Wan, Xingyu Zhai, Baoshi Qiao, Yu Kang, Bin Yu, Haohan Chen, Yang Xu, Huan Hu, Wenchao Chen, Chai Yang, Quan Yang, Ming Tian, Zheng Bian
article en
8 citations

Abstract

Ultralow-power non-volatile memristors are key elements in electronics. Generally, power reduction of memristors compromises data retention, a challenge known as the "power-retention dilemma," due to the stochastic formation of conductive dendrites in resistive-switching materials. Here, we report the results of conductive dendrite engineering in single-crystalline two-dimensional (2D) dielectrics in which directional control of filamentary distribution is possible. We find that the single-vacancy density (n<sub>SV</sub>) of single-crystalline hexagonal boron nitride (h-BN) plays an essential role in regulating conductive dendrite growth, supported by scanning joule expansion microscopy (SJEM). With optimized n<sub>SV</sub>, random dendrite growth is largely limited, and electrons hop between the neighboring Ag nanoclusters in vertical channels. The corresponding model was established to probe the relationship between n<sub>SV</sub> and memristor operating voltage. The conductive channel confinement in the vertical orientation contributes to long-retention non-volatile memristors with ultralow switch voltages (set: 26 mV; reset: -135 mV), excellent power efficiency (4 fW standby and a switching energy of 72 pJ) while keeping a high on/off resistance ratio of 10<sup>8</sup>. Even at a record-low compliance current of 10 nA, memristors retains very robust non-volatile, multiple resistive states with an operating voltage less than 120 mV (the per-transition power low as 900 pW).

The InnovationVol. 6(6)
Hong Kong Polytechnic University (HK), Zhejiang University-University of Edinburgh Institute (CN), Zhejiang University of Technology (CN), Southeast University (CN)
Affordable and clean energy
Openalex Percentile: Top 4%
Advanced Memory and Neural Computing
5.84
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