A Dual‐Branch Flux‐Based Extended Memristor Model With Machine‐Learning‐Assisted Calibration

ABSTRACT

Resistive switching devices based on complex oxide heterostructures exhibit rich, history‐dependent electrical responses arising from polarity‐selective transport mechanisms, defect dynamics, and interfacial phenomena. Accurately capturing these nonlinear behaviors remains a major challenge for predictive device modeling and for the reliable design of large‐scale memristive systems. Here, we introduce a dual‐branch, flux‐controlled extended memristor model that provides a physically interpretable asymmetric conduction in multilayer Au/HfO _x /Al ₂ O ₃ /TiO ₂ /TiN devices. The model is calibrated and validated against experimental current–voltage characteristics measured on laboratory‐fabricated devices. The approach decomposes the device memductance into two polarity‐activated branches that reproduce the distinct forward and reverse transport regimes, while embedding flux as the internal state variable governing long‐term evolution and hysteresis shaping. A hybrid machine‐learning calibration pipeline—combining Latin Hypercube Sampling, Bayesian Optimization, and gradient‐based refinement—enables robust parameter identification directly from current–voltage experimental measurements, ensuring quantitative agreement across a broad range of time‐ and frequency‐dependent excitations. The resulting model captures key physical signatures such as lobe asymmetry, history dependence, and flux accumulation, providing a unified framework suitable for device characterization, circuit‐level simulation, and neuromorphic hardware design. Owing to its modular structure and physical consistency, the methodology can be readily extended to other classes of resistive switching materials and memory devices.