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1. Improved prediction for failure time of multilayer ceramic capacitors (MLCCs): A physics-based machine learning approach

   https://doi.org/10.1063/5.0158360  

   Yousefian, Pedram ; Sepehrinezhad, Alireza ; van Duin, Adri C. ; Randall, Clive A. ( September 2023 , APL Machine Learning)

   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.

    

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2. Predicting Average Product Lifetime Using Physics-Based Gaussian Process Method in Early Design Stage

   https://doi.org/10.1115/DETC2020-22199  

   Wei, Xinpeng ; Han, Daoru ; Du, Xiaoping ( August 2020 , Proceedings of the ASME 2020 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference)

   null (Ed.)

   Average lifetime, or mean time to failure (MTTF), of a product is an important metric to measure the product reliability. Current methods of evaluating MTTF are mainly statistics or data based. They need lifetime testing on a number of products to get the lifetime samples, which are then used to estimate MTTF. The lifetime testing, however, is expensive in terms of both time and cost. The efficiency is also low because it cannot be effectively incorporated in the early design stage where many physics-based models are available. We propose to predict MTTF in the design stage by means of physics-based models. The advantage is that the design can be continually improved by changing design variables until reliability measures, including MTTF, are satisfied. Since the physics-based models are usually computationally demanding, we face a problem with both big data (on the model input side) and small data (on the model output side). We develop an adaptive supervised training method based on Gaussian process regression, and the method can then quickly predict MTTF with minimized number of calling the physics-based models. The effectiveness of the method is demonstrated by two examples.

    

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3. Physics-Based Gaussian Process Method for Predicting Average Product Lifetime in Design Stage

   https://doi.org/10.1115/1.4049509  

   Wei, Xinpeng ; Han, Daoru ; Du, Xiaoping ( August 2021 , Journal of Computing and Information Science in Engineering)

   null (Ed.)

   Abstract The average lifetime or the mean time to failure (MTTF) of a product is an important metric to measure the product reliability. Current methods of evaluating the MTTF are mainly based on statistics or data. They need lifetime testing on a number of products to get the lifetime samples, which are then used to estimate the MTTF. The lifetime testing, however, is expensive in terms of both time and cost. The efficiency is also low because it cannot be effectively incorporated in the early design stage where many physics-based models are available. We propose to predict the MTTF in the design stage by means of a physics-based Gaussian process (GP) method. Since the physics-based models are usually computationally demanding, we face a problem with both big data (on the model input side) and small data (on the model output side). The proposed adaptive supervised training method with the Gaussian process regression can quickly predict the MTTF with a reduced number of physical model calls. The proposed method can enable continually improved design by changing design variables until reliability measures, including the MTTF, are satisfied. The effectiveness of the method is demonstrated by three examples. 

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4. Utilizing time domain electrical methods to monitor MLCCs' degradation

   https://doi.org/10.1063/5.0138806  

   Yousefian, Pedram ; Rajpoot, Shalini ; Randall, Clive A. ( March 2023 , Applied Physics Letters)

   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.

    

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5. Improving Fault Tolerance for FPGA SoCs through Post-Radiation Design Analysis

   https://doi.org/10.1145/3674841  

   Wilson, Andrew Elbert ; Baker, Nathan ; Campbell, Ethan ; Wirthlin, Michael ( September 2024 , ACM Transactions on Reconfigurable Technology and Systems)

   FPGAs have been shown to operate reliably within harsh radiation environments by employing single-event upset (SEU) mitigation techniques, such as configuration scrubbing, triple-modular redundancy, error correction coding, and radiation aware implementation techniques. The effectiveness of these techniques, however, is limited when using complex system-level designs that employ complex I/O interfaces with single-point failures. In previous work, a complex SoC system running Linux applied several of these techniques only to obtain an improvement of 14\(\times\)in mean time to failure (MTTF). A detailed post-radiation fault analysis found that the limitations in reliability were due to the DDR interface, the global clock network, and interconnect. This article applied a number of design-specific SEU mitigation techniques to address the limitations in reliability of this design. These changes include triplicating the global clock, optimizing the placement of the reduction output voters and input flip-flops, and employing a mapping technique called “striping.” The application of these techniques improved MTTF of the mitigated design by a factor of 1.54\(\times\)and thus provides a 22.8X\(\times\)MTTF improvement over the unmitigated design. A post-radiation fault analysis using BFAT was also performed to find the remaining design vulnerabilities.

    

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