A Bilayer Rare‐Earth/High‐κ Oxide Memristor for Energy‐Efficient Neuromorphic Intelligence

ABSTRACT The growing demand for brain‐inspired computing systems has intensified research into energy‐efficient, scalable, and adaptive hardware that mimics biological synaptic behavior. Neuromorphic memristor devices, which integrate memory and processing functionalities within a single nanoscale unit, are emerging as promising building blocks for next‐generation artificial intelligence systems. In this work, we demonstrate a CMOS‐compatible Ag/Gd 2 O 3 /HfO 2 /Pt bilayer memristor engineered with atomically sharp interfaces and optimized defect landscapes to achieve stable and efficient resistive switching behavior. The device exhibits excellent performance, including an ON/OFF current ratio exceeding 10 7 , retention beyond 10 4 s, a sub‐microsecond switching transition time (350 ns), and low programming energy of just 13.6 pJ. Interface engineering effectively stabilizes multilevel conductance states, suppresses stochastic filament growth, and supports analog long‐term potentiation and depression. Incorporating the experimentally measured synaptic plasticity into convolutional neural network simulations yields a 78% classification accuracy on the Fashion‐MNIST dataset, along with robust color recognition. These results demonstrate, as a device‐level proof of concept, that bilayer rare‐earth/high‐κ oxide memristors can inform the development of future non‐volatile memory and low‐power edge neuromorphic systems.