UV-Enhanced Artificial Synapses Based on WSe2-SrAl2O4 Composites
Optoelectronic synapses based on transition metal dichalcogenides have received much attention as artificial synapses due to their good stability in the air and excellent photoelectric properties; however, they suffer from ultraviolet light-triggered synapses due to the ultraviolet insensitivity of transition metal dichalcogenides. In this paper, an ultraviolet-enhanced artificial synapse was achieved on WSe<sub>2</sub> combined with SrAl<sub>2</sub>O<sub>4</sub>: 6% Eu<sup>2+</sup>, 4% Dy<sup>3+</sup> phosphor. The strong ultraviolet absorption of SrAl<sub>2</sub>O<sub>4</sub>: 6% Eu<sup>2+</sup>, 4% Dy<sup>3+</sup> phosphor and radiation reabsorption are responsible for the ultraviolet-enhanced response of the WSe<sub>2</sub>-SrAl<sub>2</sub>O<sub>4</sub> synapse. The excitatory post-synaptic current of the WSe<sub>2</sub>-SrAl<sub>2</sub>O<sub>4</sub> synapse triggered by a single pulse at 365 nm was enhanced 4 times more than that from 2D WSe<sub>2</sub>, while the decay time of the post-synaptic current was 9.7 times longer than those from the WSe<sub>2</sub> device. The excellent ultraviolet sensitivity and decay time promoted the good regulation of the synaptic plasticity of the WSe<sub>2</sub>-SrAl<sub>2</sub>O<sub>4</sub> device in terms of power densities, pulse widths, pulse intervals, and pulse numbers. Furthermore, outstanding learning behavior was simulated successfully with a forgetting time of 25 s. Handwritten digit recognition was realized with 96.39% accuracy, based on the synaptic weight of the WSe<sub>2</sub>-SrAl<sub>2</sub>O<sub>4</sub> synapse. This work provides a new pathway for ultraviolet photoelectric synapse and brain-inspired computing.
Authors
- C. Chen (ORCID: https://orcid.org/0009-0009-6425-7407)
- N Zhang (ORCID: https://orcid.org/0009-0004-4510-2032)
- Qi Sun (ORCID: https://orcid.org/0009-0003-2771-867X)
- Xin Long (ORCID: https://orcid.org/0009-0005-0702-3285)
- Ping Chen (ORCID: https://orcid.org/0000-0001-6875-9337)
Institutions
- Guangxi University (CN)
Publication Details
- Journal
- Nanomaterials
- Published
- 2025-12-17
- DOI
- https://doi.org/10.3390/nano15241890
- Primary Topic
- Advanced Memory and Neural Computing
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China
- Natural Science Foundation of Guangxi Province