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

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/HfOx/Al2O3/TiO2/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.