Optimizing the analog synaptic characteristics of memristors by regulating the distribution of barrier layers

Memristors are promising candidates for artificial synapses in neuromorphic computing systems, yet their performance is often limited by nonlinear conductance modulation in oxide-based memristors. This work systematically investigates the modulation mechanism governing conductive filament (CF) rupture behavior utilizing distributed barrier layers based on finite element simulations. Our initial electro-thermal simulations of a HfO₂-based memristor with a single-layer Al₂O₃barrier (SLB) thickness (h= 0, 3, 6, 12 nm) showed only limited improvement in synaptic linearity. In contrast, the introduction of a (HfO₂/Al₂O₃)_nmultilayer barrier (MLB) structure fundamentally alters the switching dynamics. Simulations reveal that appropriately increasing the number of layers (n) promotes a transition from continuous to spatially discrete oxygen vacancy migration pathways. This engineered disorder expands the CF rupture region from a localized position to multiple interfaces, thereby reducing the electric field and temperature peaks and driving the set and reset process from abrupt to gradual switching. The optimized MLB device (n= 4) exhibits significantly enhanced synaptic linearity and analog switching characteristics, closely emulating biological synapse behavior. Furthermore, system-level validation using this device model achieved an accuracy of 94.34% in handwritten digit recognition. This work elucidates the physical mechanism by which MLBs enhance conductance linearity, providing a novel design strategy for high-performance memristive synapses.