通过栅极电压波形和带有数字栅极控制的cnn实现IGBT模块中键合线升力、电流和温度的多参数检测

Power electronic devices and components Pub Date : 2025-06-24 DOI:10.1016/j.pedc.2025.100106

Thatree Mamee , Katsuhiro Hata , Makoto Takamiya , Takayasu Sakurai , Shin-ichi Nishizawa , Wataru Saito

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引用次数: 0

摘要

提出了一种基于栅极电压波形和卷积神经网络的多参数检测方法，用于功率模块的状态监测，包括键线脱离、发射极电流和结温。该方法在各种参数充分结合的情况下，对80个水平进行了分类。此外，利用数字门控制（DGC）不仅提高了开关特性，还提高了检测精度。实验结果表明，由于组合参数的影响，栅极电压波形的灵敏度发生了显著变化。检测精度取决于DGC的控制条件，优化后的条件即使在多参数检测下也能达到96%以上的高精度。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

Multi-parameter detection of bond wire lift-off, current, and temperature in IGBT modules via gate voltage waveforms and CNNs with digital gate control

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Multi-parameter detection of bond wire lift-off, current, and temperature in IGBT modules via gate voltage waveforms and CNNs with digital gate control

A new method for multi-parameter detection of bond wire lift-off, emitter current, and junction temperature using gate voltage waveforms and a convolutional neural network is proposed for the condition monitoring of power modules. This method was demonstrated to classify 80 levels for the full combination of various parameters. In addition, digital gate control (DGC) was utilized to improve not only the switching characteristics but also the detection accuracy. The experimental results show that the sensitivity of the gate voltage waveforms changed significantly due to the influence of the combined parameters. The detection accuracy depends on the control conditions of DGC, and optimized conditions achieved a high accuracy of over 96%, even for multi-parameter detection.

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来源期刊

Power electronic devices and components Hardware and Architecture, Electrical and Electronic Engineering, Atomic and Molecular Physics, and Optics, Safety, Risk, Reliability and Quality

CiteScore

2.00

自引率

0.00%

发文量

审稿时长

80 days