Optical Synaptic Devices with Multiple Encryption Features Based on SERS‐Revealed Charge‐Transfer Mechanism

2D optical synaptic devices with atomic-scale thickness show potential for building highly integrated tunable artificial visual neural networks. However, their atomic-scale thickness also leads to weak light absorption, limiting device photoresponse. Here, a high-performance optical synaptic device based on a Rhodamine 6G (R6G)/InSe hybrid structure is proposed, achieving a remarkable 328.9% enhancement in photoresponse compared to InSe devices. Using surface-enhanced Raman spectroscopy (SERS) as a nondestructive probing technique, it is demonstrated that light-induced charge transfer between R6G and InSe is the key mechanism enabling the device's high performance. Furthermore, introducing a self-limited oxide layer on the InSe surface provides additional evidence for the charge transfer process. This charge-transfer-based device effectively mimics the neurotransmitter transmission process in biological synapses, showing unique potential in applications such as image preprocessing and decoding within artificial neural networks. In addition, through surface treatment techniques, precise control over the charge transfer process is achieved, enabling the design of a multiple encryption-based anti-counterfeiting array and highlighting their value in on-chip anti-counterfeiting. By employing a spectrally noninvasive method to probe charge transfer, this study elucidates the critical role of charge transfer in optical synaptic devices and opens novel application pathways.

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Publication Details

Journal
Advanced Materials
Published
2025-04-08
DOI
https://doi.org/10.1002/adma.202503146
Citations
16
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
11.69

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article

Optical Synaptic Devices with Multiple Encryption Features Based on SERS‐Revealed Charge‐Transfer Mechanism

Xiangyu Hou, Shaoguang Zhao, Li Tao, Yue Cheng et al.
16 citations
Advanced Materials
Advanced Memory and Neural Computing
11.69
article

Optical Synaptic Devices with Multiple Encryption Features Based on SERS‐Revealed Charge‐Transfer Mechanism

Xiangyu Hou, Shaoguang Zhao, Li Tao, Yue Cheng, Qiman Zhang, Jingwen Zhao
article en
16 citations

Abstract

2D optical synaptic devices with atomic-scale thickness show potential for building highly integrated tunable artificial visual neural networks. However, their atomic-scale thickness also leads to weak light absorption, limiting device photoresponse. Here, a high-performance optical synaptic device based on a Rhodamine 6G (R6G)/InSe hybrid structure is proposed, achieving a remarkable 328.9% enhancement in photoresponse compared to InSe devices. Using surface-enhanced Raman spectroscopy (SERS) as a nondestructive probing technique, it is demonstrated that light-induced charge transfer between R6G and InSe is the key mechanism enabling the device's high performance. Furthermore, introducing a self-limited oxide layer on the InSe surface provides additional evidence for the charge transfer process. This charge-transfer-based device effectively mimics the neurotransmitter transmission process in biological synapses, showing unique potential in applications such as image preprocessing and decoding within artificial neural networks. In addition, through surface treatment techniques, precise control over the charge transfer process is achieved, enabling the design of a multiple encryption-based anti-counterfeiting array and highlighting their value in on-chip anti-counterfeiting. By employing a spectrally noninvasive method to probe charge transfer, this study elucidates the critical role of charge transfer in optical synaptic devices and opens novel application pathways.

Advanced MaterialsVol. 37(24)
Beijing Institute of Technology (CN), National University of Singapore (SG)
National Natural Science Foundation of China, Ministry of Education, India, Beijing Institute of Technology, National Key Research and Development Program of China
Openalex Percentile: Top 1%
Advanced Memory and Neural Computing
11.69
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