Oxygen Vacancy Dynamics and Their Impact on Reliability of BaTiO3-based Multilayer Ceramic Capacitors
Restricted (Penn State Only)
- Author:
- Yousefian, Pedram
- Graduate Program:
- Materials Science and Engineering
- Degree:
- Doctor of Philosophy
- Document Type:
- Dissertation
- Date of Defense:
- June 24, 2024
- Committee Members:
- John Mauro, Program Head/Chair
Susan Trolier-McKinstry, Major Field Member
John Mauro, Major Field Member
Clive Randall, Chair & Dissertation Advisor
Yang Yang, Outside Unit & Field Member - Keywords:
- Multilayer ceramic capacitors (MLCCs)
BaTiO3
Reliability
Oxygen Vacancy
Lifetime Prediction
Machine Learning
Common Cause Failure
HALT
TSDC - Abstract:
- Multilayer ceramic capacitors (MLCCs) are crucial to the electronics industry, with a market value exceeding $23 billion and a projected compound annual growth rate of 21% based on 2022 data. These capacitors are widely used in aerospace, medical, military, and communication applications, emphasizing the need for high reliability. The ongoing advancements in capacitive volumetric density of BaTiO3-based MLCC have enabled the miniaturization of devices. However, this progress typically involves thinning the dielectric layers, which, while boosting capacitive efficiency, also generates higher electric field conditions and raises concerns about the reliability of MLCCs. 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 study critically evaluates the efficacy of mandated burn-in tests, as specified by industry standards, which are intended to identify and eliminate weak MLCCs vulnerable to early failures, commonly referred to as "infant mortality." Mandated burn-in tests, though designed to enhance reliability by screening out weak components, may not effectively detect them and can decrease the lifetime of Base Metal Electrode (BME) MLCCs by causing irreversible electromigration of oxygen vacancies. This phenomenon can lead to a vulnerable subset of capacitors prone to unexpected and potentially catastrophic failures. Given these issues, the Thermally Stimulated Depolarization Current (TSDC) technique is recommended as a promising and efficient approach for expeditious quality assessment during BME MLCC production. This technique provides useful insights into mobile defects and resistance of MLCC to oxygen vacancy electromigration, facilitating in determining the existence of potential flaws. The accuracy and limitations of existing lifetime prediction models for MLCCs, the Eyring Model (EM) and the Tipping Point Model (TPM), were critically analyzed, highlighting the necessity for improved MTTF predictions. To address these shortcomings, a novel physics-based machine learning model is introduced. This model effectively merges fundamental physical principles with machine learning techniques, offering superior performance over existing models and enabling more precise predictions of failure times, despite data limitations. The study also stresses the importance of developing comprehensive models that encompass the entire distribution of failures, moving beyond the sole reliance on MTTF to ensure that MLCCs fulfill the rigorous demands of modern electronics across various sectors. In light of these demands, the development of compact, high-density electronic devices highlights the necessity for robust root cause analysis, particularly for critical components like MLCCs. As MLCCs become central to more complex circuits, they face increased risks of both gradual and catastrophic failures, due to electrical stresses, commonly resulting from factors like oxygen vacancy electromigration, which leads to time-dependent degradation. Root cause analysis is crucial for identifying the primary causes of these failures and for designing materials that can endure operational stresses, especially in environments susceptible to thermal crosstalk and high temperatures. Furthermore, the importance of component layout on printed circuit boards (PCBs) is highlighted for managing thermal dissipation and minimizing thermal crosstalk, particularly in densely packed circuits. Techniques like Finite Element Analysis (FEA) thermal modeling, Infrared (IR) thermography, and Highly Accelerated Life Testing (HALT) illustrate how component spacing significantly influences thermal interactions, with closer spacings intensifying thermal crosstalk. Moreover, the β' factor model, utilized in these analyses, quantifies the impact of one component's failure on others. The study further explores statistical and β' factor analyses, revealing that ambient temperature does not influence thermal crosstalk and that there are exponential relationships between β', voltage, and component spacing. These findings underscore the effectiveness of strategic circuit design in mitigating risks associated with dependent failures. As electronics advance toward smaller and denser circuits, standardized failure analysis methods and advanced physical models for circuit design become increasingly crucial. These methodologies are vital for not only mitigating thermal interactions but also optimizing circuit performance and reliability. The insights gained from this research provide a strong basis for future studies aimed at refining predictive models and expanding data collection, ultimately improving the comprehensive validation of these models and their practical application.