Modulating Synaptic Plasticity of Analogue Memristor Based on Oxidized MXene Composited with ZrO <sub>2</sub> Quantum Dots
As an emerging artificial synapse for emulating the human brain, the memristor is promising owing to its excellent ability to mimic synaptic functions. In this work, we report an analogue memristor based on a composite of hydrothermally oxidized Ti3C2Tx MXene and ligand-exchanged ZrO2 quantum dots (QDs), synthesized via a solution-based method using DMF at a 1:2 weight ratio. The resulting Ag/oxidized MXene-ZrO2 QDs/FTO (fluorine-doped tin oxide) memristor exhibits a transition from digital to analogue resistive switching (RS) due to the integration of the ZrO2 QDs. It supports 18 linearly modulated conductance levels, enabling multilevel memory storage beyond 4 bits. Its reliable and reconfigurable switching behavior supports synaptic weight modulation and image recognition tasks in an artificial neural network. The synergistic interaction between oxidized MXene and ZrO2 QDs in the composite enables low-power operational analogue memristors with tunable synaptic plasticity, making it suitable for next-generation neuromorphic computing devices.
Authors
- Sang Yong Nam (ORCID: https://orcid.org/0000-0002-6056-2318)
- Prabana Jetty
- S. I. Kim (ORCID: https://orcid.org/0000-0001-8917-8822)
- Nuri Oh (ORCID: https://orcid.org/0000-0001-9145-8911)
- Somnath Suhas Kundale (ORCID: https://orcid.org/0000-0001-5354-4683)
- Namyoung Gwak
- Fernando Ordonez Morales (ORCID: https://orcid.org/0000-0002-0379-4497)
- Jun Hong Park (ORCID: https://orcid.org/0000-0001-5138-1622)
- Hee‐Soo Kim (ORCID: https://orcid.org/0000-0002-2661-7944)
- Jaeyoung Seo
- Windy Ayu Lestari
Institutions
- Seoul National University (KR)
- Gyeongsang National University (KR)
- Advanced Institute of Convergence Technology (KR)
- Convergence (US)
- Anyang University (KR)
Publication Details
- Journal
- ACS Materials Letters
- Published
- 2025-11-10
- DOI
- https://doi.org/10.1021/acsmaterialslett.5c01003
- Citations
- 1
- Primary Topic
- Advanced Memory and Neural Computing
- Type
- article
- Field-Weighted Citation Impact
- 0.73