Deep Learning-Based Prediction of Phase Evolution and Electrical Properties in Ferroelectric Thin Films

Abstract

The predictive design of ferroelectric thin films has historically been constrained by the complex coupling among composition, strain, thermal history, electric field, and microstructure. Deep learning has emerged as a promising tool to infer phase evolution and electrical property trends from multidimensional experimental and simulation data. This paper develops a system-level perspective on deep learning infrastructures for ferroelectric thin film research and manufacturing. It analyzes the architecture of predictive workflows, the integration of heterogeneous data sources, model design trade-offs, and the governance structures required for reliable deployment. Rather than treating predictive accuracy as the sole performance criterion, the discussion emphasizes robustness under process variation, fairness across materials families, interpretability for process engineers, sustainability in computational and experimental resource use, and policy mechanisms for data sharing and model qualification. Particular attention is given to the role of fabrication data from tape casting and multilayer ceramic capacitor processing as a representative integration case. The paper situates deep learning within broader materials informatics infrastructure and argues that durable progress depends on coordinated advances in data standards, physics-informed modeling, uncertainty quantification, and institutional governance.