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.
Authors
- Jinhui Song (ORCID: https://orcid.org/0000-0002-0042-2014)
- Luhua Chen (ORCID: https://orcid.org/0009-0007-2733-8801)
- Jian Chu
- Kun Zheng (ORCID: https://orcid.org/0009-0004-7292-2597)
- Hongliang Chen (ORCID: https://orcid.org/0000-0002-1432-4474)
Institutions
- Dalian University of Technology (CN)
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
Funders
- National Key Research and Development Program of China
- Dalian Science and Technology Innovation Fund