Physics-Informed Neural Network Surrogate for Oxygen Vacancy Dynamics in epitaxial $\mathrm{SrTiO_3}$ on Si memristors via Dynamic Spectral Optimization

2026-09-02

AI Summary

本文使用级联物理信息神经网络结合自定义优化器解决氧化物异质结构中的数值刚性和多尺度空间差异问题，有效模拟了Pt/SrTiO3/Si忆阻器中的离子-电子输运。

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 $μ$m 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.

Problem

• Physics-informed Neural Networks
• numerical stiffness
• multiscale spatial discrepancies
• oxygen vacancy dynamics
• SrTiO3

Innovation

• Physics-informed neural networks (PINNs)
• Cascaded PINN architecture
• Dynamic spectral optimization (DSO V2 Hybrid)
• Differentiable inverse parameter estimation
• Multiscale spatial discrepancies