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Silicon carbide (SiC) power MOSFETs are widely applied to critical infrastructure in modern energy systems. Thus, accurately predicting its remaining useful lifetime (RUL) in real-world applications has become crucial. State-of-the-art explored its RUL through a power cycling test mostly considering constant environmental conditions (e.g., fixed temperature and humidity), which has resulted in considerable RUL prediction errors in real-world applications. This study directly integrates environmental factors into the RUL modeling to address this issue. Specifically, the junction temperature (Tj), on-state voltage (Vds,on), on-state resistance (Rds,on), and case temperature (TC) have been explored in various environmental conditions to understand their tight correlations with the RUL in the real world. Time series statistics models Autoregressive (AR) and Autoregressive Integrated Moving Average (ARIMA) have been used to predict SiC MOSFET RUL to gain new insights systematically.
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Thermal Modeling and Degradation Profiling of E-Mode GaN HEMTs for Aging Characterization
Managing the thermal behavior of GaN devices under test (DUT) poses significant challenges during accelerated thermal cycling (ATC) tests, particularly due to the compact packaging of small GaN devices (e.g., QFN package) and the sharp rise in the device's RDSon at high junction temperatures. This paper presents a framework for analyzing and modeling the thermal response performance of the ATC test setup and evaluating the impact of non-linear dissipated power on the GaN DUTs. It outlines the limitations of conventional thermal sensors in accurately estimating the DUT's junction temperature through case temperature measurements under ATC conditions. The analysis and modeling of the experimental junction temperature response function shows about 4 s time constant in the measurements using a thermistor placed near the DUT, highlighting the GaN DUT's susceptibility to thermal runaway under ATC conditions (Tj−max > 125 °C), where the thermal time constant significantly exceeds the DUT's thermal transient time. Consequently, an on-state resistance (RDSon)-based Tj estimation method is employed to monitor the Tj and control the thermal cycling window boundaries effectively. Experimental investigations of several e-mode GaN HEMTs under different ATC windows are conducted to validate the ATC testing framework. Moreover, the temperature coefficient of on-state resistance (α) is characterized and quantified - considering fully packaged individual GaN DUTs’ mechanical and electrical degradation mechanisms.
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- Award ID(s):
- 2239966
- PAR ID:
- 10577637
- Publisher / Repository:
- IEEE
- Date Published:
- Journal Name:
- IEEE Open Journal of Power Electronics
- Volume:
- 5
- ISSN:
- 2644-1314
- Page Range / eLocation ID:
- 1720 to 1734
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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The state-of-the-art determines the remaining useful lifetime (RUL) through a steady-state, fixed power cycling tests (PCT) without considering the impact of dynamically changing environmental conditions. It has resulted in considerable RUL prediction errors in the real world. However, the dynamic changing conditions (e.g., large temperature swings) may affect the degradation evolution of SiC MOSFET, which could eventually result in RUL changes. Thus, it must be integrated to make accurate predictions. To precisely understand the RUL variation complexity, the junction temperature (Tj) has been measured with a Negative Thermal Coefficient (NTC) thermistor, Temperature Sensitive Electrical Parameter (TSEP), and these profiles have been modeled through the thermal model RC foster network using Extended Kalman Filter (EKF). Then, the on-state resistance (Rds,on) variations and Degradation Acceleration Factor (DAF) under the dynamic environment conditions are integrated into a lifetime prediction model to accurately predict the RUL through the Long Short-Term Memory (LSTM) machine learning algorithm.more » « less
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1109/OJPEL.2024.3481056
