Physics-Informed Neural Network Surrogate for Oxygen Vacancy Dynamics in epitaxial SrTiO3 on Si memristors via Dynamic Spectral Optimization

Physics-informed neural networks (PINNs) offer a promising framework for modeling semiconductor devices, yet standard architectures struggle with severe numerical stiffness and multiscale spatial discrepancies inherent to oxide heterostructures. Here, we demonstrate a cascaded PINN architecture coupled with a custom second-order Chebyshev second kind polynomial spectral optimizer (DSO V2 Hybrid) to model ion-electronic drift-diffusion transport in Pt/SrTiO3/Si memristive heterostructures across a 20 nm STO film on a 380 μm Si substrate. By isolating potential, carrier density, and vacancy transport into four sequentially trained sub-neural-networks, our model circumvents condition numbers exceeding 1016 without operator splitting. The trained surrogate reproduces experimental conductive-AFM current-voltage hysteresis (R2 > 0.96) while ensuring strict Poisson consistency across continuous space. Compared to conventional finite-element solvers (e.g., COMSOL), the PINN surrogate enables differentiable inverse parameter estimation and linear time inference.