Reservoir‐Driven Neuromorphic Computing Based on Composite Rare‐Earth/Transition Metal Oxide Memristor

Abstract

The growing demand for efficient hardware platforms for artificial intelligence has motivated the exploration of memristive devices capable of combining memory and computation. Here, a composite oxide memristor based on an Ag/Gd 2 O 3 :Nb 2 O 5 /Pt structure is fabricated via co‐sputtering and investigated for neuromorphic and reservoir‐computing demonstrations. The device exhibits stable bipolar resistive switching with a large memory window of ∼10 9 and relatively low operating voltages, showing average SET and RESET voltages of +0.16 ± 0.03 V and −0.07 ± 0.05 V, respectively. Pulse‐based electrical measurements show progressive conductance modulation and enable the emulation of synaptic plasticity behaviors, including long‐term potentiation (LTP), long‐term depression (LTD), paired‐pulse facilitation (PPF), and post‐tetanic potentiation (PTP). A 4‐bit pulse encoding scheme generates sixteen distinguishable electrical states that remain reproducible over repeated cycles and across multiple devices. The experimentally measured conductance responses are further incorporated into a device‐aware simulation framework for image classification using the CIFAR‐100 dataset. When combined with a CNN feature extractor, the reservoir‐enhanced model achieves 79% classification accuracy, approaching the 81% accuracy of the ideal digital baseline. These results show that experimentally measured memristor states can be incorporated into device‐aware learning frameworks, providing a practical approach for exploring memristor‐assisted neuromorphic computing strategies.