提升功率器件结温监测精度，关键在改进瞬态TSEP校准方法。
A High-accuracy Calibration Method of Transient TSEPs for Power Semiconductor Devices
- 基于热分析补偿负载电流引起的温差，提升校准精度。
- 识别并建模寄生参数耦合效应，减少系统误差。
- 用神经网络捕捉随机误差规律，适合工业级结温监测。
热敏电参数（TSEP）法通过监测结温来提升功率器件可靠性，其核心包含校准、回归与应用三个环节。尽管回归算法和TSEP灵敏度已趋成熟，但校准方法对精度的影响常被忽视。本文提出一种高精度瞬态TSEP校准方法：首先基于热分析引入温度补偿策略，缓解双脉冲测试中负载电流导致的温差；其次分析杂散参数影响，识别出传统方法忽略的耦合参数；再者发现随机误差呈对数正态分布，存在隐藏变量，故采用神经网络构建结温预测模型。以阈值电压为例进行实验验证，相比传统方法，平均绝对误差降低超30%。该方法无需额外硬件成本，具备良好泛化能力。
原文摘要 · Abstract (English)
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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