Bias Configurable Se<sub>0.25</sub>Te<sub>0.75</sub>/Si Photodiode Array for Multi‐Channel Encrypted Communication

Abstract The transmission of image data in the Internet of Things (IoT) faces growing challenges in security and privacy protection. Traditional optical encryption methods, which typically rely on single‐ or dual‐channel signal processing, are insufficient for large‐scale, high‐throughput data transmission, necessitating advanced solutions. Here, the study presents a multi‐channel encryption strategy based on bias‐selective configuration, realized through an on‐chip integrated multi‐channel pixel‐level image encryption system using a Se 0.25 Te 0.75 /Si photodiode array as the encryption hardware. By calibrating the silent‐state bias as the encryption key, the system transcends the constraints of dual‐channel approaches, enabling scalable encryption for n ≥ 3 channels. This method is demonstrated in the high‐security transmission of both static and dynamic images. Notably, the three‐channel system achieves a remarkable entropy increase of 1.3 and a correlation coefficient of 0.069, providing a significant enhancement in encryption strength over traditional single or dual‐channel systems. With the expansion of the detector array size and the number of optical channels, the encryption level can be further amplified. This work introduces a transformative paradigm for optical communication encryption, with profound implications for fields such as autonomous driving, IoT, and other security‐critical applications.

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Institutions

Publication Details

Journal
Advanced Functional Materials
Published
2025-03-07
DOI
https://doi.org/10.1002/adfm.202425559
Citations
16
Primary Topic
Neural Networks and Reservoir Computing
Type
article
Field-Weighted Citation Impact
39.70

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article

Bias Configurable Se0.25Te0.75/Si Photodiode Array for Multi‐Channel Encrypted Communication

Wenbo Luo, Jiangchao Han, Yunlu Lian, Jun Gou et al.
16 citations
Advanced Functional Materials
Neural Networks and Reservoir Computing
39.70
article

Bias Configurable Se0.25Te0.75/Si Photodiode Array for Multi‐Channel Encrypted Communication

Wenbo Luo, Jiangchao Han, Yunlu Lian, Jun Gou, Jun Wang, Zongkai Yan, He Yu, Chong Yang, Yadong Jiang, Shengwang Jia
article en
16 citations

Abstract

Abstract The transmission of image data in the Internet of Things (IoT) faces growing challenges in security and privacy protection. Traditional optical encryption methods, which typically rely on single‐ or dual‐channel signal processing, are insufficient for large‐scale, high‐throughput data transmission, necessitating advanced solutions. Here, the study presents a multi‐channel encryption strategy based on bias‐selective configuration, realized through an on‐chip integrated multi‐channel pixel‐level image encryption system using a Se 0.25 Te 0.75 /Si photodiode array as the encryption hardware. By calibrating the silent‐state bias as the encryption key, the system transcends the constraints of dual‐channel approaches, enabling scalable encryption for n ≥ 3 channels. This method is demonstrated in the high‐security transmission of both static and dynamic images. Notably, the three‐channel system achieves a remarkable entropy increase of 1.3 and a correlation coefficient of 0.069, providing a significant enhancement in encryption strength over traditional single or dual‐channel systems. With the expansion of the detector array size and the number of optical channels, the encryption level can be further amplified. This work introduces a transformative paradigm for optical communication encryption, with profound implications for fields such as autonomous driving, IoT, and other security‐critical applications.

Advanced Functional MaterialsVol. 35(32)
University of Electronic Science and Technology of China (CN), Henan Tianguan Group (China) (CN)
National Natural Science Foundation of China, China Postdoctoral Science Foundation, Aeronautical Science Foundation of China, National Key Research and Development Program of China
Openalex Percentile: Top 0%
Neural Networks and Reservoir Computing
39.70
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