Dual Randomness From Spatial and Temporal Variations in Stochastic Zinc Gallate Nanoclusters for PUF and TRNG Co‐Driven Image Generation

ABSTRACT

We propose a novel dual‐randomness framework that integrates physically unclonable functions (PUFs) and true random number generators (TRNGs) within a single nanomaterial platform. In contrast to conventional approaches that treat PUFs and TRNGs as separate entities, our platform integrates both entropy sources within a single stochastic nanomaterial system, enabling parallel and co‐localized operation. The mixed zinc gallate (ZGO) nanoclusters (NCs) present stochastic properties in which size‐dependent variations influence device resistance, capacitance, and noise characteristics. These variations lead to unique device signatures, while time‐dependent noise fluctuations serve as a TRNG source. The proof as a PUF is confirmed by the difference in position through the measurement of the work function, capacitance, and impedance. The device‐derived TRNG bitstreams from 20 devices exceeded the acceptance threshold of 0.01 for all 11 reported NIST criteria. By integrating the stochastic characteristics arising from the spatially dependent and time‐dependent properties from mixed ZGO NCs, the dilemma of a single device is solved. To demonstrate hardware‐derived latent‐vector modulation, experimentally measured current traces from the mixed‐ZGO devices were converted into a 512‐dimensional latent vector and supplied to a pretrained StyleGAN2‐AFHQv2 generator.