Exploring the Inherent Variability of Economically Fabricated ZnO Devices Towards Physical Unclonable Functions for Secure Authentication

Meeting the rising need for secure authentication in IoT and Industry 4.0, this work presents chemically synthesized ZnO nanostructured homojunctions as powerful and scalable physical unclonable functions (PUFs). By leveraging intrinsic variability from Li doping and the stochastic hydrothermal growth process, we systematically identified electrical parameters offering outstanding variability, stability, and reproducibility. ZnO devices outperform commercial diodes by delivering richer parameter diversity, lower costs, and superior environmental sustainability. Pushing beyond traditional approaches, we introduce multi-level quantization for boosted accuracy and entropy, demonstrate the normal distribution of challenge candidate parameters to support a novel method under development, and extract multiple parameters (8-10) per device instead of relying on a single-bit output. Parameter optimization and selection are performed upfront through a rigorous assessment of variability and inter-correlation, maximizing uniqueness and reliability. Thanks to their strong scalability and eco-friendliness, ZnO-based homojunctions emerge as a dynamic, future-proof platform for building low-cost, high-security, and sustainable digital identity systems.