The effects of BaZrO₃ addition on the structural and electrical properties of BaTiO₃-based base-metal-electrode (BME) multilayer ceramic capacitor (MLCC) ceramics were investigated. Dielectric compositions containing 0–9 wt% BaZrO₃ were fabricated using a conventional MLCC process, and their crystal structure, dielectric behavior, temperature stability, frequency stability, and insulation resistance were evaluated. X-ray diffraction and Rietveld refinement confirmed the formation of a single-phase perovskite structure for all compositions. Increasing BaZrO₃ content resulted in a systematic shift of diffraction peaks toward lower angles and an increase in unit-cell volume from 64.61 to 66.05 ų, indicating successful Zr incorporation into the BaTiO₃ lattice. The dielectric permittivity increased with BaZrO₃ addition and reached a maximum value of approximately 2,110 at 6 wt%, while dielectric loss decreased significantly. Capacitance stability against temperature and frequency variations was markedly improved with increasing BaZrO₃ content. In addition, insulation resistance increased substantially over the investigated temperature range, demonstrating enhanced electrical reliability.

The rapid proliferation of artificial intelligence (AI) servers and high-performance computing systems has significantly elevated the technical and reliability requirements for multilayer ceramic capacitors (MLCCs). In such systems, MLCCs are critical passive components that must deliver high capacitance, fast transient response, and robust insulation performance under high temperature, voltage, and current density. This review examines the material, structural, and process innovations that underpin MLCC performance in AI applications. Key topics include the development of ultrathin dielectric layers (<0.5 μm), rare-earth doped BaTiO₃-based dielectrics with enhanced DC bias stability, and core-shell microstructures designed for temperature and field resilience. The paper also explores insulation degradation mechanisms―such as vacancydriven conduction and demixing―and advanced reliability assessment methodologies, including HALT, TSDC, and the tipping point framework. Comparisons with automotive-grade MLCCs highlight the unique requirements of AI systems, such as ultraminiaturization, high volumetric efficiency, and ppm-level field failure rates. Finally, the review discusses emerging trends in MLCC technology, including particle engineering, interface stabilization, and advanced lamination techniques, and provides insight into the future direction of capacitor development tailored to AI data center environments.

Citations

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