Model Releases
Physics-Informed Neural Network Surrogate for Oxygen Vacancy Dynamics in epitaxial SrTiO_3 on Si memristors via Dynamic Spectral Optimization
arXiv:2609.02966v1 Announce Type: cross Abstract: Physics-informed neural networks (PINNs) offer a promising framework for modeling semiconductor devices, yet standard architectures struggle with seve
arXiv:2609.02966v1 Announce Type: cross Abstract: 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/SrTiO_3/Si memristive heterostructures across a 20 nm STO film on a 380 mum Si substrate. By isolating potential, carrier density, and vacancy transport into four sequentially trained sub-neural-networks, our model circumvents condition numbers exceeding 10^{16} without operator splitting. The trained surrogate reproduces experimental conductive-AFM current-voltage hysteresis (R^2 > 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.
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Source: arXiv cs.LG | 2026-09-04