Monitoring Initial Solder Layer Degradation in a Multichip IGBT Module via Combined TSEPs

Yang, Jiangrong; Che, Yanbo; Li, Ran; Du, Mingxing; Jiang, Huaping; Xiao, Muzixiang

doi:10.1109/tdmr.2021.3130274

Cited by 14 publications

(3 citation statements)

References 31 publications

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“…) where ε is the semiconductor dielectric constant, k is the Boltzmann constant, T is the temperature, NA is the semiconductor reference concentration, ni is the carrier concentration, Cox is the gate oxide capacitance, Eg is the band gap width, and b is the constant factor. Since the effect of carrier concentration on Vth is greater than the effect of temperature on Vth, Vth exhibits a negative temperature dependence [29]. When using the linear extrapolation method to calculate Vth, it is necessary to draw the transfer characteristic curves of the IGBT chip when operating in the linear and saturation regions.…”

Section: Threshold Voltagementioning

confidence: 99%

Bond wires aging monitoring for IGBT module based on junction temperature difference of TSEPs

Liu

Yin³

et al. 2023

IEICE Electron. Express

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In this paper, we propose to use the junction temperature difference estimated by two temperature-sensitive electric parameters (TSEPs), threshold voltage, and p-n junction forward voltage of the IGBT module to monitor the aging of the bond wires. When the bond wires are lifted off, the chip surface temperature and the overall temperature gradient will increase, making the temperature difference measured by these two TSEPs larger. A new method is proposed in this paper to monitor the aging of the bonding wires of IGBT modules by the difference between them, and the correctness of the proposed method is verified by experiments on IGBT modules with different degrees of bonding wire aging.

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Section: Threshold Voltagementioning

confidence: 99%

Bond wires aging monitoring for IGBT module based on junction temperature difference of TSEPs

Liu

Yin³

et al. 2023

IEICE Electron. Express

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“…There are two existing methods for monitoring the status of solder layer: model-based method and datadriven method [5,16]. Based on the model method, the parametric failure physical model is used to determine the material characteristics and the aging of packaging factors [17].…”

Section: Introductionmentioning

confidence: 99%

Monitoring method of solder layer void damage of IGBT module based on transfer function

Huang,

Sun,

Wang

et al. 2024

IEICE Electron. Express

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This paper proposes a method based on transfer function to monitor the solder layer void damage in IGBT modules. Firstly, the thermal resistance of each layer structure of IGBT module and the Cauer model is analyzed. Secondly, the process of obtaining the parameters of Cauer model by transfer function is analyzed. Finally, the thermal resistance of the IGBT module under different void fractions of the solder layer is measured by experiments, which obtains the relationship between the thermal resistance difference and the void fractions. In addition, this method is compared with the monitoring method by IEC standard, which verifies the accuracy and feasibility of this method.

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“…Therefore, it is important to accurately predict the junction temperature of IGBT to improve its reliability. In past studies, the temperature-sensitive parameter (TSP) method was often used to obtain the junction temperature of IGBTs [16,17]. This approach is often based on artificial intelligence algorithms [18,19].…”

Section: Introductionmentioning

confidence: 99%

Junction Temperature Prediction of Insulated-Gate Bipolar Transistors in Wind Power Systems Based on an Improved Honey Badger Algorithm

et al. 2022

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To reduce carbon dioxide emissions, wind power generation is receiving more attention. The conversion of wind energy into electricity requires frequent use of insulated-gate bipolar transistors (IGBTs). Therefore, it is important to improve their reliability. This study proposed a method to predict the junction temperature of IGBTs, which helps to improve their reliability. Limited by the bad working environment, the physical temperature measurement method proposed by previous research is difficult to apply. Therefore, a junction temperature prediction method based on an extreme learning machine optimized by an improved honey badger algorithm was proposed in this study. First, the data of junction temperature were obtained by the electro-heat coupling model method. Then, the accuracy of the proposed method was verified with the data. The results show that the average absolute error of the proposed method is 0.0303 °C, which is 10.62%, 11.14%, 91.67%, and 95.54% lower than that of the extreme learning machine optimized by a honey badger algorithm, extreme learning machine optimized by a seagull optimization algorithm, extreme learning machine, and back propagation neural network model. Therefore, compared with other models, the proposed method in this paper has higher prediction accuracy.

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Monitoring Initial Solder Layer Degradation in a Multichip IGBT Module via Combined TSEPs

Cited by 14 publications

References 31 publications

Bond wires aging monitoring for IGBT module based on junction temperature difference of TSEPs

Bond wires aging monitoring for IGBT module based on junction temperature difference of TSEPs

Monitoring method of solder layer void damage of IGBT module based on transfer function

Junction Temperature Prediction of Insulated-Gate Bipolar Transistors in Wind Power Systems Based on an Improved Honey Badger Algorithm

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