Mo<i><sub>x</sub></i>Re<sub>(1−</sub><i><sub>x</sub></i><sub>)</sub>S<sub>2</sub>‐Based Optoelectronic Synapse for Artificial Neural Visual System Application

Abstract Optoelectronic synapses, constructed from 2D transition metal dichalcogenides (TMDs) with atomic‐scale thickness, offer substantial benefits for the development of high‐density integrated artificial neural visual systems (ANVSs). Effective charge capture and retention are essential for realizing synaptic memory function. Although charge trapping is realized by gate‐voltage‐regulated three‐terminal devices or interface‐modulated heterostructures, they involve high energy consumption and complex device structures. Another approach is to introduce material defects by post‐processing methods to trap carriers, but this method inevitably damages the pristine materials and requires intricate fabrication processes. In this study, the chemical vapor deposition (CVD) method is employed to directly introduce Re atoms as point defects into MoS 2 , which act as trap centers for capturing carriers, eliminating the need for complex device structures or post‐processing procedures. The resultant Mo x Re (1− x ) S 2 nanofilms and nanoribbons exhibit excellent optoelectronic synaptic properties and broadband photoresponse characteristics respectively, due to varying doping concentrations. The optoelectronic synapses based on Mo x Re (1− x ) S 2 nanofilms can simulate human color recognition, attributed to their differential response to varying light wavelengths, and possess image preprocessing capabilities for noise reduction and contrast enhancement. The study provides a simple approach for fabricating optoelectronic synapses, thereby facilitating the integration of 2D TMDs into ANVSs.

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Publication Details

Journal
Advanced Functional Materials
Published
2024-09-27
DOI
https://doi.org/10.1002/adfm.202411999
Citations
19
Primary Topic
Neural Networks and Reservoir Computing
Type
article
Field-Weighted Citation Impact
6.60

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article

MoxRe(1−x)S2‐Based Optoelectronic Synapse for Artificial Neural Visual System Application

Chengbao Jiang, Si Chen, Xiaona Sun, Zhengjie Wang et al.
19 citations
Advanced Functional Materials
Neural Networks and Reservoir Computing
6.60
article

MoxRe(1−x)S2‐Based Optoelectronic Synapse for Artificial Neural Visual System Application

Chengbao Jiang, Si Chen, Xiaona Sun, Zhengjie Wang, Shengxue Yang
article en
19 citations

Abstract

Abstract Optoelectronic synapses, constructed from 2D transition metal dichalcogenides (TMDs) with atomic‐scale thickness, offer substantial benefits for the development of high‐density integrated artificial neural visual systems (ANVSs). Effective charge capture and retention are essential for realizing synaptic memory function. Although charge trapping is realized by gate‐voltage‐regulated three‐terminal devices or interface‐modulated heterostructures, they involve high energy consumption and complex device structures. Another approach is to introduce material defects by post‐processing methods to trap carriers, but this method inevitably damages the pristine materials and requires intricate fabrication processes. In this study, the chemical vapor deposition (CVD) method is employed to directly introduce Re atoms as point defects into MoS 2 , which act as trap centers for capturing carriers, eliminating the need for complex device structures or post‐processing procedures. The resultant Mo x Re (1− x ) S 2 nanofilms and nanoribbons exhibit excellent optoelectronic synaptic properties and broadband photoresponse characteristics respectively, due to varying doping concentrations. The optoelectronic synapses based on Mo x Re (1− x ) S 2 nanofilms can simulate human color recognition, attributed to their differential response to varying light wavelengths, and possess image preprocessing capabilities for noise reduction and contrast enhancement. The study provides a simple approach for fabricating optoelectronic synapses, thereby facilitating the integration of 2D TMDs into ANVSs.

Advanced Functional MaterialsVol. 35(1)
Beihang University (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 3%
Neural Networks and Reservoir Computing
6.60
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