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

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.