Intelligent Microstructure–Property Modeling of Perovskite Oxide Dielectrics for Miniaturized Electronic Devices
Keywords:
perovskite oxide dielectrics, microstructure-property modeling, machine learning, materials informatics, multilayer ceramic capacitors, data governance, robust deployment, sustainabilityAbstract
The escalating demand for miniaturized electronic devices has placed severe constraints on dielectric materials, particularly perovskite oxide ceramics used in multilayer capacitors, tunable filters, and embedded passive components. The performance of these materials depends on a complex interplay among grain size, domain structure, defect chemistry, interface quality, and processing history. Traditional microstructure-property modeling approaches often fail to capture these multiscale nonlinear interactions with sufficient speed and fidelity for modern device development. Intelligent modeling frameworks that integrate machine learning, high-throughput experiments, and domain-aware representations offer a promising route toward accelerated discovery and deployment. This paper presents a systems-level examination of intelligent microstructure-property modeling for perovskite oxide dielectrics. It considers data infrastructure, multiscale representation, model architecture, structural trade-offs, deployment constraints, robustness, fairness, sustainability, and governance. The discussion emphasizes that successful implementation requires more than algorithmic sophistication. It demands coordinated infrastructure for data provenance, interoperable metadata, reliable uncertainty quantification, and lifecycle-aware design. The paper also examines how advanced fabrication data for ultrathin film dielectrics can inform model development while cautioning against overreliance on narrow datasets. By treating the modeling pipeline as a socio-technical system, the paper identifies structural tensions between edge-level inference and cloud-scale training, between model complexity and interpretability, and between open innovation and proprietary material data. The concluding perspective outlines future directions including self-driving laboratories, multimodal foundation models, and adaptive governance frameworks that can support sustainable miniaturization of electronic components.
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