Programmable Physical Unclonable Functions Using Randomly Anisotropic Two-Dimensional Flakes

Abstract

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 Er₃O₄Cl 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 Er₃O₄Cl patterns gives freedom to the PUFs and ensures a high encoding capacity of 2³⁸⁰⁰⁰⁰. 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.