Material‐Intrinsic Circularly Polarized Light Encryption via Neuromorphic Transistors

ABSTRACT Brain‐inspired neuromorphic computing provides an efficient paradigm for integrating sensing, memory, and processing, offering new opportunities for secure optical information systems. Circularly polarized light (CPL) is an attractive carrier for information encryption. However, existing CPL‐based systems are fundamentally constrained by the lack of chiral semiconductors with broadband, consistently high absorption dissymmetry factor ( g abs ), and by encoding strategies that rely on externally defined optical parameters. Here, we propose a material‐centric encryption paradigm enabled by newly designed heterochiral molecules that exhibit uniformly high g abs (>0.01) across nearly all bisignate circular dichroism bands. This broadband and intrinsic chiroptical response allows CPL to function as a robust, high‐dimensional encoding variable. Leveraging this capability, we develop a multi‐wavelength CPL encryption and decryption framework based on chiral optoelectronic synaptic transistors that integrate sensing, memory, and processing within a single device. Compared to conventional single‐wavelength schemes, our approach significantly enhances encryption performance, achieving a 21% increase in entropy, a 134% improvement in normalized signal‐to‐noise ratio, and a 33% enhancement in signal difference maximization index. An artificial neural network constructed from these devices demonstrates 93.0% accuracy in decrypting dual‐encrypted video. This work establishes a shift from parameter‐defined to material‐intrinsic multidimensional encoding for neuromorphic optical security systems.

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Publication Details

Journal
Advanced Functional Materials
Published
2026-06-29
DOI
https://doi.org/10.1002/adfm.76738
Primary Topic
Neural Networks and Reservoir Computing
Type
article
Field-Weighted Citation Impact
0.00

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article

Material‐Intrinsic Circularly Polarized Light Encryption via Neuromorphic Transistors

Huipeng Chen, Lixuan Liu, Zhixiang Wei, Jianqi Zhang et al.
Advanced Functional Materials
Neural Networks and Reservoir Computing
article

Material‐Intrinsic Circularly Polarized Light Encryption via Neuromorphic Transistors

Huipeng Chen, Lixuan Liu, Zhixiang Wei, Jianqi Zhang, Changsong Gao, Yongjie Cai, Weilong Huang, Yang Yang, Xiaolong Li
article en

Abstract

ABSTRACT Brain‐inspired neuromorphic computing provides an efficient paradigm for integrating sensing, memory, and processing, offering new opportunities for secure optical information systems. Circularly polarized light (CPL) is an attractive carrier for information encryption. However, existing CPL‐based systems are fundamentally constrained by the lack of chiral semiconductors with broadband, consistently high absorption dissymmetry factor ( g abs ), and by encoding strategies that rely on externally defined optical parameters. Here, we propose a material‐centric encryption paradigm enabled by newly designed heterochiral molecules that exhibit uniformly high g abs (>0.01) across nearly all bisignate circular dichroism bands. This broadband and intrinsic chiroptical response allows CPL to function as a robust, high‐dimensional encoding variable. Leveraging this capability, we develop a multi‐wavelength CPL encryption and decryption framework based on chiral optoelectronic synaptic transistors that integrate sensing, memory, and processing within a single device. Compared to conventional single‐wavelength schemes, our approach significantly enhances encryption performance, achieving a 21% increase in entropy, a 134% improvement in normalized signal‐to‐noise ratio, and a 33% enhancement in signal difference maximization index. An artificial neural network constructed from these devices demonstrates 93.0% accuracy in decrypting dual‐encrypted video. This work establishes a shift from parameter‐defined to material‐intrinsic multidimensional encoding for neuromorphic optical security systems.

Advanced Functional Materials
National Center for Nanoscience and Technology (CN), Tan Kah Kee Innovation Laboratory (CN), Fuzhou University (CN)
National Natural Science Foundation of China, Natural Science Foundation of Fujian Province
Openalex Percentile: Top 6%
Neural Networks and Reservoir Computing
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