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Metal-oxide-semiconductor field-effect Transistors, or MOSFETs, play a critical role in electronic circuits. They are primarily utilized for amplifying and switching signals.
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The Metal-Oxide-Semiconductor Field-Effect Transistor (MOSFET) plays a pivotal role in modern electronics thanks to its versatility and efficiency in controlling electrical currents. This device, also known as IGFET, MISFET, and MOSFET, has three main terminals: the Source, Drain, and Gate. MOSFETs are classified into n-channel or p-channel types based on the doping characteristics of their substrate and the source or drain regions.
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Related Experiment Video

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A Junction Temperature Prediction Method Based on Multivariate Linear Regression Using Current Fall Characteristics

Haihong Qin1, Yang Zhang1, Yu Zeng2

  • 1College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China.

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|August 14, 2025
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Summary

Accurately estimating Silicon carbide (SiC) MOSFET junction temperature (Tj) is crucial. This study introduces a new method using turn-off current fall time and fall loss for precise Tj monitoring in power electronics.

Keywords:
condition monitoringcurrent fall energy losscurrent fall timejunction temperaturetemperature-sensitive electrical parameter (TSEP)

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Area of Science:

  • Power Electronics
  • Semiconductor Devices
  • Thermal Management

Background:

  • Junction temperature (Tj) is critical for Silicon carbide (SiC) MOSFETs, impacting performance and reliability.
  • Traditional methods using single temperature-sensitive electrical parameters (TSEPs) struggle with accuracy due to limited temperature sensitivity.
  • Accurate Tj estimation is vital for condition monitoring and reliability in power electronic systems.

Purpose of the Study:

  • To develop an accurate and practical method for estimating SiC MOSFET junction temperature (Tj).
  • To utilize complementary TSEPs, specifically turn-off current fall time (t_fi) and fall loss (E_fi), for improved Tj prediction.
  • To enable advanced condition monitoring and intelligent sensing in power electronics.

Main Methods:

  • Demonstrated the feasibility of using current fall time and fall energy loss as TSEPs.
  • Developed a coupled junction temperature prediction model using multivariate linear regression (MLR).
  • Employed t_fi and E_fi as input variables within the MLR model for Tj estimation.

Main Results:

  • Experimental validation confirmed the high prediction accuracy of the proposed MLR method.
  • The approach effectively leverages the current fall phase characteristics of SiC MOSFETs.
  • Demonstrated superiority over traditional single-TSEP methods.

Conclusions:

  • The proposed MLR-based method provides accurate SiC MOSFET junction temperature (Tj) estimation.
  • This technique enhances condition monitoring and reliability assessment in power electronic systems.
  • Offers significant potential for intelligent sensing applications in power electronics.