Noise is typically seen as a "detrimental" factor in electronic devices, and much research has focused on mitigating its effects. However, instead of eliminating it, recent efforts have been made to harness noise for emerging applications such as neuromorphic computing, probability computing, and true random number generation. Despite its fascinating potential, noise often operates under low △R/R conditions, making integration with circuits challenging. In this study, we present a high-frequency graphene-based stochastic current generator. This generator produces a stochastic current with an on-off ratio exceeding 10, a nearly equal probability for the upper and lower states over 10 3 seconds, and extremely low energy consumption (below 250 μJ/s). The oscillatory current is generated via stochastic resonance from the imperfect Pauli blocking of interband transitions near the graphene Dirac Point. Additionally, Markov chains, time-lag patterns, and Kalman filtering are employed to explore the transition states, probabilities, and predictability of the stochastic currents.
Ma et al. (Sun,) studied this question.