Noise‐Driven Functionality in Neuromorphic Materials, Devices, and Systems

Neuromorphic hardware turns the physical dynamics of functional materials into computation, enabling dense in‐memory and probabilistic architectures beyond digital abstraction. Yet this advantage also makes neuromorphic systems highly sensitive to noise and variability, because analog device states directly encode synaptic weights, neuronal thresholds, membrane currents, and spike signals. While device‐to‐device variation, cycle‐to‐cycle variation, and drift have been widely discussed as critical non‐idealities, temporal noise, particularly read‐current fluctuations during sensing of analog states, has received less systematic attention. This review addresses this gap by positioning temporal noise within the broader landscape of device variability and by highlighting low‐frequency noise spectroscopy as a mechanism‐resolved tool for identifying fluctuation origins, spectral signatures, time scales, and computational consequences. We establish a framework that classifies neuromorphic noise according to its role: suppression, tolerance, and utilization: (1) noise suppression reduces intrinsic fluctuation sources to secure multilevel precision and computing accuracy; (2) noise tolerance maintains robust operation through device‐to‐system strategies; and (3) noise utilization transforms intrinsic stochasticity into primitives for stochastic neurons and synapses, entropy generation, probabilistic bits, Bayesian inference, and reinforcement learning. Across neuromorphic materials, devices, and systems, this review reframes temporal noise from a reliability‐limiting imperfection into a hardware‐native resource for adaptive neuromorphic intelligence.