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

2025-01-09

AI Summary

To address the low calibration accuracy of transient sensitive electrical parameters (TSEPs) for junction temperature monitoring in power semiconductor devices, this paper proposes a high-precision TSEP calibration method. We introduce a novel thermal-analysis-based temperature compensation strategy that reveals and decouples parasitic parameter coupling effects; further, we identify that random calibration errors follow a log-normal distribution and incorporate this insight into an error model. Leveraging thermoelectric coupling features and the log-normal error prior, we design a lightweight neural network for junction temperature prediction. Validated using threshold voltage, the method reduces mean absolute error by over 30% without requiring additional hardware and demonstrates strong generalizability across operating conditions. The proposed approach significantly enhances TSEP calibration reliability, establishing a new paradigm for non-invasive, high-accuracy junction temperature monitoring in power electronic systems.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Planning, Routing, and Scheduling: Temporal Planning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Security and Privacy: Large-scale security measurements
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs

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.

Problem

• Power Semiconductors
• Transient TSEPs Accuracy
• Temperature Monitoring Reliability

Innovation

• Calibration Method
• Neural Network Prediction
• Thermal Sensitive Electrical Parameters (TSEPs)