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Base metal electrode (BME) multilayer ceramic capacitors (MLCCs) are widely used in aerospace, medical, military, and communication applications, emphasizing the need for high reliability. The ongoing advancements in BaTiO3-based MLCC technology have facilitated further miniaturization and improved capacitive volumetric density for both low and high voltage devices. However, concerns persist regarding infant mortality failures and long-term reliability under higher fields and temperatures. To address these concerns, a comprehensive understanding of the mechanisms underlying insulation resistance degradation is crucial. Furthermore, there is a need to develop effective screening procedures during MLCC production and improve the accuracy of mean time to failure (MTTF) predictions. This article reviews our findings on the effect of the burn-in test, a common quality control process, on the dynamics of oxygen vacancies within BME MLCCs. These findings reveal the burn-in test has a negative impact on the lifetime and reliability of BME MLCCS. Moreover, the limitations of existing lifetime prediction models for BME MLCCs are discussed, emphasizing the need for improved MTTF predictions by employing a physics-based machine learning model to overcome the existing models’ limitations. The article also discusses the new physical-based machine learning model that has been developed. While data limitations remain a challenge, the physics-based machine learning approach offers promising results for MTTF prediction in MLCCs, contributing to improved lifetime predictions. Furthermore, the article acknowledges the limitations of relying solely on MTTF to predict MLCCs’ lifetime and emphasizes the importance of developing comprehensive prediction models that predict the entire distribution of failures. 

Multilayer ceramic capacitors (MLCC) play a vital role in electronic systems, and their reliability is of critical importance. The ongoing advancement in MLCC manufacturing has improved capacitive volumetric density for both low and high voltage devices; however, concerns about long-term stability under higher fields and temperatures are always a concern, which impact their reliability and lifespan. Consequently, predicting the mean time to failure (MTTF) for MLCCs remains a challenge due to the limitations of existing models. In this study, we develop a physics-based machine learning approach using the eXtreme Gradient Boosting method to predict the MTTF of X7R MLCCs under various temperature and voltage conditions. We employ a transfer learning framework to improve prediction accuracy for test conditions with limited data and to provide predictions for test conditions where no experimental data exists. We compare our model with the conventional Eyring model (EM) and, more recently, the tipping point model (TPM) in terms of accuracy and performance. Our results show that the machine learning model consistently outperforms both the EM and TPM, demonstrating superior accuracy and stability across different conditions. Our model also exhibits a reliable performance for untested voltage and temperature conditions, making it a promising approach for predicting MTTF in MLCCs.

 

Adding excessive metal oxide doping to a powder batch is a known way to compensate for the loss of volatile cation species during high temperature sintering. An important case in the piezoelectric ceramics is the bismuth oxide in the lead-free ferroelectric ceramic bismuth sodium titanate (BNT). Building from the earlier knowledge about excessive bismuth oxide's influences on the properties of BNT, we further note that varying the sintering temperature can both control the distribution of excessive Bi3+ and impact the relaxor/normal ferroelectric behaviors and corresponding phase transition. In addition to the nature of polarization, the sintering temperature also significantly manipulates the electrical conductivity. A hypothetical mechanism for the resistive grain boundary is proposed, based on inferences from electrical—microstructure—processing relations in 85% Bi0.5Na0.5TiO3-15% BaTiO3 with batched Bi2O3 excess and acceptor Mg2+ in a co-doped strategy.

 

To fulfill the demands of more bandwidth in 5G and 6G communication technology, new dielectric substrates that can be co‐fired into packages and devices that have low dielectric loss and improved thermal conductivity are desired. The motivation for this study is to design composites with low dielectric loss (tan δ) and high thermal conductivity (κ), while still limiting the electrical conductivity, for microwave applications involving high power and high frequency. This work describes the fabrication of high‐density electroceramic composites with a model dielectric material for cold sintering, namely sodium molybdate (Na₂Mo₂O₇), and fillers with higher thermal conductivity such as hexagonal boron nitride. The physical properties of the composites were characterized as a function of filler vol.%, temperature, and frequency. Understanding the variation in measured properties is achieved through analyzing the respective transport mechanisms.

 

The continued development of BaTiO3-based multilayer ceramic capacitors has contributed to further miniaturization by reducing the thickness of each dielectric layer for different voltage range components. MLCC designs that achieve higher volumetric capacitive efficiency must be balanced with stable properties over long operational times at higher fields and temperatures, raising concerns about their reliability. To improve the reliability and slow transient mechanisms of oxygen vacancy electromigration that drive the degradation of insulation resistance of MLCCs, we need to develop new models and improved metrologies to enhance the performance of MLCCs. This paper demonstrates how electrical characterization techniques, such as thermally stimulated depolarization current and highly accelerated life test, can be used to better understand MLCCs' degradation and assess their reliability. Also, the limitations of existing lifetime prediction models and their shortcomings of using mean time to failure in predicting the lifetime of MLCCs are discussed along with future perspectives on evaluating the reliability of MLCCs.

 

By applying atomic force microscope to the flat in-plane polycrystalline microstructure, pressure-dependent topographic evolutions can be studied with respect to surface dihedral angle and groove geometry. Using a cold-sintered zinc oxide densified at 200 °C as a model system, this study demonstrates an experimental methodology for the quantification of relative grain boundary energetics in cold-sintered material systems and an associated geometric model for connecting the morphological change and underlying mechanochemical phenomenon at various uniaxial pressures ranging from 70 to 475 MPa. Depending on the applied pressure, the anisotropic grain growth, normal grain growth, and coarsening of particles are distinctively observed according to the changes in the groove geometry, suggesting that the growth kinetics can be considered as a function of pressure.