Deep Learning-Based Process–Property Mapping for Multilayer Ceramic Capacitor Manufacturing
Keywords:
multilayer ceramic capacitors, process–property mapping, deep learning, smart manufacturing, uncertainty quantification, data governance, sustainability, industrial policyAbstract
Multilayer ceramic capacitors are foundational components in modern electronics, and their performance depends on tightly coupled chemical, thermal, mechanical, and electrical processes distributed across complex manufacturing lines. Deep learning-based process–property mapping has emerged as a promising approach for predicting dielectric behavior, reliability, and yield from process parameters and sensor streams. This paper examines the system-level implications of such mapping in multilayer ceramic capacitor manufacturing rather than treating it as a purely algorithmic problem. It discusses the material and process context, including tape casting, electrode deposition, lamination, sintering, termination, and testing, and explains why data-driven models must reconcile multiscale physics with industrial variability. The paper then analyzes neural architecture choices, hybrid physics-informed approaches, data infrastructure, edge and cloud deployment, model maintenance, uncertainty quantification, failure-aware control, governance, fairness, and sustainability. It argues that successful deployment requires more than high predictive accuracy. It depends on interoperable data pipelines, calibrated uncertainty, robust validation under distribution shift, clear accountability, and policies that address intellectual property, labor, environmental impact, and equitable access to advanced manufacturing intelligence. Cross-domain comparisons with semiconductor fabrication, battery production, and pharmaceutical manufacturing clarify both opportunities and limits. The conclusion emphasizes that deep learning can strengthen process–property mapping only when embedded within a broader socio-technical architecture that treats models as governed infrastructure rather than isolated predictive tools.
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This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
