Random fractal-enabled physical unclonable functions with dynamic AI authentication

Ningfei Sun, Ziyu Chen, Yanke Wang, Shu Wang, Yong Xie () and Qian Liu ()
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Ningfei Sun: Beihang University
Ziyu Chen: Beihang University
Yanke Wang: Karlsruhe Institute of Technology
Shu Wang: National Center for Nanoscience and Technology & University of Chinese Academy of Sciences
Yong Xie: Beihang University
Qian Liu: National Center for Nanoscience and Technology & University of Chinese Academy of Sciences

Nature Communications, 2023, vol. 14, issue 1, 1-10

Abstract: Abstract A physical unclonable function (PUF) is a foundation of anti-counterfeiting processes due to its inherent uniqueness. However, the self-limitation of conventional graphical/spectral PUFs in materials often makes it difficult to have both high code flexibility and high environmental stability in practice. In this study, we propose a universal, fractal-guided film annealing strategy to realize the random Au network-based PUFs that can be designed on demand in complexity, enabling the tags’ intrinsic uniqueness and stability. A dynamic deep learning-based authentication system with an expandable database is built to identify and trace the PUFs, achieving an efficient and reliable authentication with 0% “false positives”. Based on the roughening-enabled plasmonic network platform, Raman-based chemical encoding is conceptionally demonstrated, showing the potential for improvements in security. The configurable tags in mass production can serve as competitive PUF carriers for high-level anti-counterfeiting and data encryption.

Date: 2023
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DOI: 10.1038/s41467-023-37588-5

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