HfO2/Y2O3 Bilayer Memristor with Gradual Conductance Modulation for Neuromorphic Computing and Artificial Synapse

Abstract

Memristors are promising candidates for neuromorphic computing, but conventional oxide-based memristors suffer abrupt switching and limited synaptic plasticity. This paper reports a W/HfO2/Y2O3/Pt memristor that overcomes limitations through oxygen vacancy gradient and heterojunction interface engineering. The HfO2 layer (40.79% oxygen vacancies) forms primary conductive filaments, whereas the Y2O3 layer (33.68%) contains discontinuous residual filament segments that enable analog switching. Band discontinuity at the heterojunction localizes filament rupture, reducing filament randomness and improving switching uniformity. Combined with the oxygen vacancy gradient, this structure enables gradual synaptic modulation. The device exhibits gradual conductance modulation by adjusting the compliance current or reset voltage, with five stable resistance states retained for over 104 s. Under 50 consecutive negative pulses, the conductance decreases, demonstrating long-term depression behavior. A neural network simulation achieves 97.8% accuracy in MNIST handwritten digit recognition. This study promotes controllable conductance modulation and provides an effective strategy for artificial synapses and neuromorphic computing.