From Synthesis to Kinetics: A Data-Driven Deep Learning Framework for Process-Aware Ferroelectric Dynamics

Changhao Wang, Xinhao Yao, danyang chen, Chaobo Li, xiuyan li, sicong yuan, hanzhi xun, Said Hamdioui, Nicolò Bellarmino, Riccardo Cantoro

Keywords: Ferroelectric HfZrO2, Deep Learning, Process-Property Mapping

TL;DR: Ferroelectric HfZrO2, Deep Learning, Process-Property Mapping, Wake-up Effect, Transfer Learning, Polarization Kinetics, Ferroelectric Memory, Inverse Material Design

Abstract:

Physical modeling of emerging ferroelectric $Hf_{x}Zr_{1-x}O_{2}$ (HZO) materials is constrained by incomplete theoretical descriptions of domain kinetics and the difficulty of formulating efficient equations for complex evolutionary mechanisms, such as the wake-up effect.
To address this challenge, we propose a data-driven Deep Learning framework utilizing an optimized Residual Artificial Neural Network (ResANN). By incorporating critical fabrication parameters as direct inputs, our model learns the intrinsic process-property mappings directly from experimental data.
While standard ML-based approaches often leverage idealized simulation data, this work expands the scope by rigorously training on large-scale device measurement data, thus establishing a direct bridge between fabrication parameters and material behavior.
By leveraging an optimized deep learning framework and Transfer Learning, our methodology demonstrates exceptional capability in capturing highly non-linear polarization dynamics.
We show that this approach yields superior accuracy (Adjusted $R^2 \approx 0.998$) compared to traditional physical equations, effectively predicting hysteresis evolution and achieving zero-shot generalization to untrained material thicknesses.
Consequently, this framework serves as a predictive tool for inverse material design, guiding the optimization of synthesis protocols for next-generation ferroelectrics materials.

Submission Track: Full Paper

Submission Category: AI-Guided Design + Automated Material Characterization

Submission Number: 7