Optical Memristors Based on Homojunctions of Zinc Oxide Quantum Dots and Nanowires

Optical memristive devices have garnered significant attention due to their potential applications in artificial intelligence. Currently, nanoscale semiconductor materials are widely employed to fabricate high-performance optical memristors. However, these devices suffer from performance limitations, including low switching ratios and discontinuous hysteresis curves. In this study, we innovatively designed and fabricated a high-performance optical photomemristor based on the homojunction of zinc oxide (ZnO) nanowires (NWs) and quantum dots (QDs). ZnO QDs increase the number of electrical contact points in the functional layer, and the interfaces between NWs and QDs act as carrier reservoirs for O2– and Zn2+ ions, significantly enhancing the switching ratio. Under illumination and bias, the electric field redistributes and intensifies around the QDs, accelerating the migration of the O2– ions and facilitating the formation of conductive channels by Zn2+, rendering the device’s hysteresis curves more continuous than those of ZnO NWs-based devices. Experimental results demonstrate that the device exhibits a switching ratio of 102, an order of magnitude higher than that of traditional ZnO NWs photomemristors, with markedly improved hysteresis curve continuity. COMSOL simulations validate the mechanism of electric field redistribution and enhancement. This research presents an effective strategy for developing high-performance neural memristive devices using homojunctions of semiconductor nanomaterials.

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Publication Details

Journal
ACS Applied Nano Materials
Published
2025-07-04
DOI
https://doi.org/10.1021/acsanm.5c01750
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
0.00

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article

Optical Memristors Based on Homojunctions of Zinc Oxide Quantum Dots and Nanowires

Jinhui Song, Luhua Chen, Jian Chu, Kun Zheng et al.
ACS Applied Nano Materials
Advanced Memory and Neural Computing
article

Optical Memristors Based on Homojunctions of Zinc Oxide Quantum Dots and Nanowires

Jinhui Song, Luhua Chen, Jian Chu, Kun Zheng, Hongliang Chen
article en

Abstract

Optical memristive devices have garnered significant attention due to their potential applications in artificial intelligence. Currently, nanoscale semiconductor materials are widely employed to fabricate high-performance optical memristors. However, these devices suffer from performance limitations, including low switching ratios and discontinuous hysteresis curves. In this study, we innovatively designed and fabricated a high-performance optical photomemristor based on the homojunction of zinc oxide (ZnO) nanowires (NWs) and quantum dots (QDs). ZnO QDs increase the number of electrical contact points in the functional layer, and the interfaces between NWs and QDs act as carrier reservoirs for O2– and Zn2+ ions, significantly enhancing the switching ratio. Under illumination and bias, the electric field redistributes and intensifies around the QDs, accelerating the migration of the O2– ions and facilitating the formation of conductive channels by Zn2+, rendering the device’s hysteresis curves more continuous than those of ZnO NWs-based devices. Experimental results demonstrate that the device exhibits a switching ratio of 102, an order of magnitude higher than that of traditional ZnO NWs photomemristors, with markedly improved hysteresis curve continuity. COMSOL simulations validate the mechanism of electric field redistribution and enhancement. This research presents an effective strategy for developing high-performance neural memristive devices using homojunctions of semiconductor nanomaterials.

ACS Applied Nano MaterialsVol. 8(28)
Dalian University of Technology (CN)
National Key Research and Development Program of China, Dalian Science and Technology Innovation Fund
Affordable and clean energy
Openalex Percentile: Top 86%
Advanced Memory and Neural Computing
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Optical Memristors Based on Homojunctions of Zinc Oxide Quantum Dots and Nanowires — Jinhui Song, Luhua Chen, et al. · ACS Applied Nano Materials (2025) | TGRS Research Map | TGRS