ABSTRACT Recent advances in computing including security applications, Monte Carlo simulations, and probabilistic computing, have increased the demand for robust probabilistic elements. Ion‐motion‐mediated volatile memristors with threshold switching (TS) characteristics have emerged as promising physical entropy sources because of their stochastic conductive filament (CF) formation and rupture. However, optimizing a memristor as an entropy source requires a material system that actively promotes ion motion and the associated CF formation/rupture, along with a quantitative understanding of their coupled electrothermal behavior. In this study, by integrating a porous nanorods (NRs)‑based oxide layer that enhances ion‐motion pathways, we achieved rapid, device‑centric digital and analog random outputs without the need for post‑processing. Moreover, we directly visualized the stochastic dynamics of multiple CFs using scanning thermal microscopy (SThM) and verified our findings through electrothermal simulations, confirming the device's inherent randomness. Finally, a bimodal (digital and analog) true random number generator (TRNG) and a probabilistic computing platform demonstrated the versatility of TS memristors as tunable and robust sources of randomness for probability‐oriented applications.
Soh et al. (Wed,) studied this question.