Multi‐Mode 2D CuInP ₂ S ₆ ‐Based Memristors Enabling Intrinsic Dropout Spiking Neural Networks

ABSTRACT The growing demand for efficient information processing in the era of AI and IoT urgently calls for neuromorphic hardware capable of alleviating the von Neumann bottleneck. Here, we report a Ti/Au/CuInP 2 S 6 /Ag/Au multi‐mode memristor that naturally integrates three distinct resistive switching modes: volatile analog, volatile digital, and non‐volatile digital. The volatile analog mode, driven by Cu‐ion migration, naturally emulates leaky integrate‐and‐fire (LIF) neuronal dynamics. Meanwhile, Ag filament–based digital switching shows stochastic transitions between volatile and non‐volatile states, tunable via compliance current. By directly mapping these switching modes onto core neuromorphic functions, a spiking neural network with built‐in dropout is realized with drastically simplified circuit architectures. This work demonstrates how multi‐mode memristive physics can unify neuron activation and dropout functionality within a single device, offering a compact and hardware‐efficient pathway toward robust neuromorphic computing systems.