Physics-Informed Neural Network Surrogate for Oxygen Vacancy Dynamics in epitaxial SrTiO3 on Si memristors via Dynamic Spectral Optimization
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/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.
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BibTeX
@misc{podorozhny2026physicsinformed,
title = {{Physics-Informed Neural Network Surrogate for Oxygen Vacancy Dynamics in epitaxial $\mathrmSrTiO_3$ on Si memristors via Dynamic Spectral Optimization}},
author = {Rodion Podorozhny and Nikoleta Theodoropoulou and Jelena Tešić},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.02966},
eprint = {2609.02966},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.02966},
}
RIS
TY - UNPB
TI - Physics-Informed Neural Network Surrogate for Oxygen Vacancy Dynamics in epitaxial SrTiO_3 on Si memristors via Dynamic Spectral Optimization
AU - Rodion Podorozhny
AU - Nikoleta Theodoropoulou
AU - Jelena Tešić
PY - 2026
DA - 2026/09/02
PB - arXiv
DO - 10.48550/arxiv.2609.02966
UR - https://arxiv.org/abs/2609.02966
AB - 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 μm 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.
ER -