Chen Ping, Li Dongyan, Li Zexin, Xu Xiang, Wang Haoyun, Zhou Xing, Zhai Tianyou
State Key Laboratory of Materials Processing and Die & Mould Technology, School of Materials Science and Engineering, Huazhong University of Science and Technology (HUST), Wuhan 430074, People's Republic of China.
School of Materials Science and Engineering, Hefei University of Technology, Hefei 230009, People's Republic of China.
ACS Nano. 2023 Dec 12;17(23):23989-23997. doi: 10.1021/acsnano.3c08740. Epub 2023 Nov 20.
Physical unclonable functions (PUFs) have been developed as promising strategies for secure authentication. Conventional strategies of PUFs have a limitation in the aspect of security for their static single channel. The introduction of polarization will endow a static PUF with many dynamic transformations based on polarized properties. Herein, high-security PUFs based on the polarized luminescence of chaotic luminescent patterns are fabricated by anisotropic rare earth (RE) material ErOCl flakes. These derivatives under different polarizations show strong randomness (with similarity of the same PUF as high as 97%, while that for different PUFs is as low as 44%), which further widens the security and capacity of PUFs. Polarized luminescence control of ErOCl patterns gives freedom to the PUFs and ensures a high encoding capacity of 2. Furthermore, we build a convolutional neural network (CNN) to realize fast intelligent authentication by extracting image features for convolution operation with a very high accuracy of 99.8% and fast classification speed in only 5 epochs.
物理不可克隆函数（PUF）已被开发为用于安全认证的有前景的策略。传统的PUF策略在其静态单通道的安全性方面存在局限性。偏振的引入将基于偏振特性赋予静态PUF许多动态变换。在此，通过各向异性稀土（RE）材料ErOCl薄片制备了基于混沌发光图案偏振发光的高安全性PUF。这些衍生物在不同偏振下表现出很强的随机性（同一PUF的相似度高达97%，而不同PUF的相似度低至44%），这进一步拓宽了PUF的安全性和容量。ErOCl图案的偏振发光控制赋予了PUF自由度，并确保了2的高编码容量。此外，我们构建了一个卷积神经网络（CNN），通过提取图像特征进行卷积运算来实现快速智能认证，准确率高达99.8%，且仅在5个训练周期内就能实现快速分类。
