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.

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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

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article

UV-Enhanced Artificial Synapses Based on WSe2-SrAl2O4 Composites

C. Chen, N Zhang, Qi Sun, Xin Long et al.
Nanomaterials
Advanced Memory and Neural Computing
article

UV-Enhanced Artificial Synapses Based on WSe2-SrAl2O4 Composites

C. Chen, N Zhang, Qi Sun, Xin Long, Ping Chen
article en

Abstract

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.

NanomaterialsVol. 15(24)
Guangxi University (CN)
National Natural Science Foundation of China, Natural Science Foundation of Guangxi Province
Openalex Percentile: Top 59%
Advanced Memory and Neural Computing
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