Morphology‐Defined Entropy Streams for Random‐Bit Generation and Probabilistic Sampling in Memristor Arrays

ABSTRACT Physical entropy sources are essential for hardware security and probabilistic information processing, yet most memristor‐based random‐number generators rely on stochastic switching outputs without identifying a designable materials‐level parameter that controls how intrinsic device stochasticity is expressed. Here, we present a morphology‐engineered entropy platform based on Ag/TiO 2 /Au nanoisland (NI)/Pt electrochemical memristor arrays, in which the Au NI morphology acts as a tunable structural modulator of the stochastic high‐resistance‐state transport landscape. Thickness‐controlled Volmer–Weber growth produces distinct NI regimes that reshape local‐field hotspots and candidate filament pathways through which residual filament configurations, interfacial defects, and stochastic ion migration are translated into HRS‐current dispersion. NI‐1.5 provides a practical entropy‐engineering window where broad hotspot diversity and recoverable HRS states are balanced. The morphology‐modulated HRS dispersion is converted into entropy streams in a 32 × 32 array through paired‐current comparison and validated using bit probability, binary entropy, min‐entropy, restart/reproducibility tests, autocorrelation, NIST SP 800–22 tests, and machine‐learning‐based predictability evaluation. The validated stream further serves as a stochastic sampling primitive for Monte Carlo probabilistic inference. This work establishes nanoscale morphology as a designable materials‐level control parameter linking stochastic memristive transport, array‐level randomness, and probabilistic sampling.