Prognostics of Power MOSFETs under Thermal Stress Accelerated Aging using Data-Driven and Model-Based Methodologies
An approach for predicting remaining useful life of power MOSFETs (metal oxide field effect transistor) devices has been developed. Power MOSFETs are semiconductor switching devices that are instrumental in electronics equipment such as those used in operation and control of modern aircraft and spacecraft. The MOSFETs examined here were aged under thermal overstress in a controlled experiment and continuous performance degradation data were collected from the accelerated aging experiment. Die- attach degradation was determined to be the primary failure mode. The collected run-to-failure data were analyzed and it was revealed that ON-state resistance increased as die-attach degraded under high thermal stresses. Results from finite element simulation analysis support the observations from the experimental data. Data-driven and model based prognostics algorithms were investigated where ON-state resistance was used as the primary precursor of failure feature. A Gaussian process regression algorithm was explored as an example for a data-driven technique and an extended Kalman filter and a particle filter were used as examples for model-based techniques. Both methods were able to provide valid results. Prognostic performance metrics were employed to evaluate and compare the algorithms.
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Brown, D., Abbas, M., Ginart, A., Ali, I., Kalgren, P., & Vachtsevanos, G. (2010). Turn-off Time as a Precursor for Gate Bipolar Transistor Latch-up Faults in Electric Motor Drives. Paper presented at the Annual Conference of the Prognostics and Health Management Society 2010.
Celaya, J., Kulkarni, C., Biswas, G., & Goebel, K. (2011a).Towards Prognostics of Electrolytic Capacitors.Paper presented at the AIAA Infotech@Aerospace,
St. Louis, MO.
Celaya, J., Saxena, A., Wysocki, P., Saha, S., & Goebel, K.(2010a). Towards Prognostics of Power MOSFETs: Accelerated Aging and Precursors of Failure. Paper presented at the Annual Conference of the Prognostics and Health Management Society 2010.
Celaya, J. R., Patil, N., Saha, S., Wysocki, P., & Goebel, K. (2009). Towards Accelerated Aging Methodologies and Health Management of Power MOSFETs (Technical Brief). Paper presented at the Annual Conference of the Prognostics and Health Management Society 2009.
Celaya, J. R., Saxena, A., Vashchenko, V., Saha, S., & Goebel, K. (2011b). Prognostics of Power MOSFET. Paper presented at the 23nd International Symposium on Power Semiconductor Devices & IC's (ISPSD), San Diego, CA.
Celaya, J. R., Wysocki, P., Vashchenko, V., Saha, S., & Goebel, K. (2010b). Accelerated aging system for prognostics of power semiconductor devices. Paper presented at the 2010 IEEE AUTOTESTCON.
Ginart, A., Roemer, M., Kalgren, P., & Goebel, K. (2008).Modeling Aging Effects of IGBTs in Power Drives by Ringing Characterization. Paper presented at the IEEE International Conference on Prognostics and Health Management.
Ginart, A. E., Ali, I. N., Celaya, J. R., Kalgren, P. W., Poll, S. D., & Roemer, M. J. (2010). Modeling SiO2 Ion Impurities Aging in Insulated Gate Power Devices Under Temperature and Voltage Stress. Paper presented at the Annual Conference of the Prognostics and Health Management Society 2010.
Goebel, K., Saha, B., & Saxena, A. (2008). A Comparison of Three Data-Driven Techniques for Prognostics. Paper presented at the Proceedings of the 62nd Meeting of the Society For Machinery Failure Prevention Technology (MFPT).
Gordon, N. J., Salmond, D. J., & Smith, A. F. M. (1993). Novel Approach to Nonlinear/Non-GaussianBayesian State Estimation. IEE Proceedings Radar and Signal Processing, 140(2), 107-113.
Meinhold, R. J., & Singpurwalla, N. D. (1983). Understanding the Kalman Filter. The American Statistician, 37(2), 123-127.
Patil, N., Celaya, J., Das, D., Goebel, K., & Pecht, M.(2009). Precursor Parameter Identification for Insulated Gate Bipolar Transistor (IGBT) Prognostics. IEEE Transactions on Reliability, 58(2), 276.
Rasmussen, C. E., & Williams, C. K. I. (2006). Gaussian Processes for Machine Learning.
Saha, B., Celaya, J. R., Wysocki, P. F., & Goebel, K. F. (2009a). Towards prognostics for electronics components. Paper presented at the Aerospace conference, 2009 IEEE.
Saha, B., Goebel, K., & Christophersen, J. (2009b). Comparison of prognostic algorithms for estimating remaining useful life of batteries. Transactions of the Institute of Measurement and Control, 31(3-4), 293-308. doi: 10.1177/0142331208092030
Saha, S., Celaya, J. R., Vashchenko, V., Mahiuddin, S., & Goebel, K. F. (2011). Accelerated Aging with Electrical Overstress and Prognostics for Power MOSFETs. Paper presented at the IEEE EnergyTech 2011.
Saxena, A., Celaya, J., Balaban, E., Goebel, K., Saha, B., Saha, S., & Schwabacher, M. (2008, 6-9 Oct. 2008). Metrics for evaluating performance of prognostic techniques. Paper presented at the Prognostics and Health Management, 2008. PHM 2008. International Conference on.
Sonnenfeld, G., Goebel, K., & Celaya, J. R. (2008). An agile accelerated aging, characterization and scenario simulation system for gate controlled power transistors. Paper presented at the IEEE AUTOTESTCON 2008.
Welch, G., & Bishop, G. (2006). An Introduction to the Kalman Filter (TR 95-041): Department of Computer Science, University of North Carolina at Chapel Hill.
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