一种用于功率半导体器件瞬态 TSEP 的高精度校准方法
A High-accuracy Calibration Method of Transient TSEPs for Power Semiconductor Devices
AI总结：
针对瞬态 TSEP 校准易受温差、杂散参数和随机误差影响的问题，提出热补偿、耦合参数分析与神经网络建模方法，使阈值电压结温预测 MAE 降低逾 30%。
摘要：
热敏感电参数（TSEP）方法通过结温监测来提高功率器件的可靠性，具有关键作用。TSEP 方法包括校准、回归和应用三个关键过程。尽管已有大量工作致力于改进回归算法并提高 TSEP 灵敏度，以提升结温监测精度，但这些途径已遇到瓶颈。实际上，校准方法会显著影响监测精度，而这一方面在传统 TSEP 方法中常被忽视。为解决该问题，本文提出一种用于瞬态 TSEP 的高精度校准方法。首先，引入一种基于热分析的温度补偿策略，以缓解双脉冲测试期间负载电流引起的温差。其次，分析杂散参数的影响，以识别现有方法通常忽略的耦合参数。第三，观察到随机误差服从对数高斯分布，其中包含一个隐变量。使用神经网络获得结温预测模型。以阈值电压为例，对所提出的校准方法进行了实验验证。与传统校准方法相比，平均绝对误差降低了 30% 以上。此外，该方法不需要额外硬件成本，并具有良好的泛化能力。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
