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基于多物理场仿真辅助光学测量的多芯片碳化硅金属氧化物半导体场效应晶体管模块热阻抗特性分析

Thermal Impedance Characterization Using Optical Measurement Assisted by Multi-Physics Simulation for Multi-Chip SiC MOSFET Module.

作者信息

Kim Min-Ki, Yoon Sang Won

机构信息

Department of Automotive Engineering, Hanyang University, Seoul 04763, Korea.

出版信息

Micromachines (Basel). 2020 Nov 30;11(12):1060. doi: 10.3390/mi11121060.

DOI:10.3390/mi11121060
PMID:33265986
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7760757/
Abstract

In this paper, an approach to determine the thermal impedance of a multi-chip silicon carbide (SiC) power module is proposed, by fusing optical measurement and multi-physics simulations. The tested power module consists of four parallel SiC metal-oxide semiconductor field-effect transistors (MOSFETs) and four parallel SiC Schottky barrier diodes. This study mainly relies on junction temperature measurements performed using fiber optic temperature sensors instead of temperature-sensitive electrical parameters (TESPs). However, the fiber optics provide a relatively slow response compared to other available TSEP measurement methods and cannot detect fast responses. Therefore, the region corresponding to undetected signals is estimated via multi-physics simulations of the power module. This method provides a compensated cooling curve. We analyze the thermal resistance using network identification by deconvolution (NID). The estimated thermal resistance is compared to that obtained via a conventional method, and the difference is 3.8%. The proposed fusion method is accurate and reliable and does not require additional circuits or calibrations.

摘要

本文提出了一种通过融合光学测量和多物理场模拟来确定多芯片碳化硅(SiC)功率模块热阻的方法。所测试的功率模块由四个并联的SiC金属氧化物半导体场效应晶体管(MOSFET)和四个并联的SiC肖特基势垒二极管组成。本研究主要依赖于使用光纤温度传感器进行结温测量,而非温度敏感电参数(TESP)。然而,与其他可用的TESP测量方法相比,光纤的响应相对较慢,无法检测快速响应。因此,通过对功率模块进行多物理场模拟来估计未检测信号对应的区域。该方法提供了一条补偿冷却曲线。我们使用去卷积网络识别(NID)来分析热阻。将估计的热阻与通过传统方法获得的热阻进行比较,差异为3.8%。所提出的融合方法准确可靠,无需额外的电路或校准。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b15d/7760757/ddbf41a4032d/micromachines-11-01060-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b15d/7760757/17d146c015bf/micromachines-11-01060-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b15d/7760757/673831e75d04/micromachines-11-01060-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b15d/7760757/5e6946d31655/micromachines-11-01060-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b15d/7760757/4cf24995fba8/micromachines-11-01060-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b15d/7760757/466f71141918/micromachines-11-01060-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b15d/7760757/2f00189c96df/micromachines-11-01060-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b15d/7760757/89f92b0b8acc/micromachines-11-01060-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b15d/7760757/b72d0da55366/micromachines-11-01060-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b15d/7760757/ddbf41a4032d/micromachines-11-01060-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b15d/7760757/17d146c015bf/micromachines-11-01060-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b15d/7760757/673831e75d04/micromachines-11-01060-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b15d/7760757/5e6946d31655/micromachines-11-01060-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b15d/7760757/4cf24995fba8/micromachines-11-01060-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b15d/7760757/466f71141918/micromachines-11-01060-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b15d/7760757/2f00189c96df/micromachines-11-01060-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b15d/7760757/89f92b0b8acc/micromachines-11-01060-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b15d/7760757/b72d0da55366/micromachines-11-01060-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b15d/7760757/ddbf41a4032d/micromachines-11-01060-g009.jpg

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引用本文的文献

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Micromachines (Basel). 2022 Mar 18;13(3):463. doi: 10.3390/mi13030463.