SNUEI

Actual Maximum Junction Temperature Estimation Process of Multichip SiC MOSFET Power Modules With New Calibration Method and Deep Learning

2022 · IEEE Journal of Emerging and Selected Topics in Power Electronics

저자

Min Ki Kim, Young-Doo Yoon, Sang Won Yoon

연구주제

Silicon Carbide Semiconductor Technologies · GaN-based semiconductor devices and materials

NTIS 과학기술표준분류

에너지/자원만에너지·환경·원자력 확장

지원 과제·AI 키워드로 잡을 근거가 없어 분류하지 않았다.

AI 연구 키워드·방법

anti parallel diode junction temperature estimation multi chip power module silicon carbide mosfet temperature sensitive electrical parameter

딥러닝 광학 측정

키워드 (OpenAlex)

multi-chip power modules anti-parallel diodes temperature-sensitive electrical parameters dynamic calibration fiber optic temperature sensor on-resistance

초록

This article proposes a new estimation process of the actual maximum junction temperature in multichip silicon carbide (SiC) MOSFET power modules. Temperature-sensitive electrical parameters (TSEPs) are common methods to estimate the junction temperature of power devices, but they have limitations in specifying the actual maximum junction temperature in multichip power modules with antiparallel diodes. Common TSEPs include body-diode forward voltage or ON-resistance of MOSFETs, but it is difficult to distinguish the impact of the antiparallel diodes and the ON-resistance-based method has low sensitivity under low-current calibration. In particular, the TSEPs have difficulty to calibrate the actual maximum junction temperature of a power module, which should solely detect the hottest device in the module. Therefore, a new dynamic calibration method is proposed using fiber optics while sim…

이 논문을 지원한 국가R&D 과제 (NTIS 성과)

연도과제부처사업수행기관NTIS 분류(1순위)
2023미래자동차-소프트웨어 융복합 혁신인재 양성 교육연구단교육부4단계두뇌한국21사업한양대학–

인접 그래프

노드 hover = 이웃 강조 · 1회 클릭 = 고정 · 고정된 노드 다시 클릭 = 페이지 이동

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교수 1

윤상원

연구주제 1

Silicon Carbide Semiconductor Technologies

방법 2

딥러닝 · 광학 측정