Noise-Tunable Memristor Enabling Programmable Probabilistic Neurons for Frequency-Selective Time-Series Signal...

Memristors exhibit tunable resistance, which has been widely exploited in non-volatile memory, in-memory computing, and neuromorphic computing. They can also serve as an entropy source due to their inherent instability, making them attractive for security devices and probabilistic computing. When these two characteristics are coupled, memristors can act as tunable entropy sources; however, this direction remains largely unexplored. Here, we propose a spiking-rate-programmable probabilistic neuron that leverages the tunable noise characteristics of a Ru/TaO_x/Pt memristor. In this memristor, the conduction mechanism varies across resistance states, leading to distinct noise behaviors and signal-to-noise ratios that depend on the programmed resistance. This noise can be harnessed to realize frequency-selective, frequency-domain probabilistic neural encoding. By integrating these probabilistic neurons, an identical network architecture can process input signals spanning a wide frequency range, achieving around 95% classification performance on both low-frequency human activity data (UCI HAR, 0.4-25 Hz) and high-frequency speech data (Audio MNIST, 20 Hz-8 kHz). These results highlight a new direction that leverages the intrinsic properties of memristors for compact, adaptive, and energy-efficient time-series encoding.