A High-accuracy Calibration Method of Transient TSEPs for Power Semiconductor Devices

• URL: http://arxiv.org/abs/2501.05005v1
• Date: Thu, 09 Jan 2025 06:56:47 GMT
• Title: A High-accuracy Calibration Method of Transient TSEPs for Power Semiconductor Devices
• Authors: Qinghao Zhang, Wenrui Li, Pinjia Zhang,
• Abstract summary: The thermal sensitive electrical parameter (TSEP) method is crucial for enhancing the reliability of power devices. We propose a high-accuracy calibration method for transient TSEPs. Compared with conventional calibration methods, the mean absolute error is reduced by over 30%.
• Score: 2.7446241148152257
• License:
• Abstract: The thermal sensitive electrical parameter (TSEP) method is crucial for enhancing the reliability of power devices through junction temperature monitoring. The TSEP method comprises three key processes: calibration, regression, and application. While significant efforts have been devoted to improving regression algorithms and increasing TSEP sensitivity to enhance junction temperature monitoring accuracy, these approaches have reached a bottleneck. In reality, the calibration method significantly influences monitoring accuracy, an aspect often overlooked in conventional TSEP methods. To address this issue, we propose a high-accuracy calibration method for transient TSEPs. First, a temperature compensation strategy based on thermal analysis is introduced to mitigate the temperature difference caused by load current during dual pulse tests. Second, the impact of stray parameters is analyzed to identify coupled parameters, which are typically neglected in existing methods. Third, it is observed that random errors follow a logarithm Gaussian distribution, covering a hidden variable. A neural network is used to obtain the junction temperature predictive model. The proposed calibration method is experimental validated in threshold voltage as an example. Compared with conventional calibration methods, the mean absolute error is reduced by over 30%. Moreover, this method does not require additional hardware cost and has good generalization.

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