High-Frequency Non-Invasive Magnetic Field-Based Condition Monitoring of SiC Power MOSFET Modules

Javad Naghibi, Kamyar Mehran and Martin P. Foster
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Javad Naghibi: School of Electronics Engineering and Computer Science, Queen Mary University of London, London E1 4NS, UK
Kamyar Mehran: School of Electronics Engineering and Computer Science, Queen Mary University of London, London E1 4NS, UK
Martin P. Foster: Department of Electronic and Electrical Engineering, The University of Sheffield, Sheffield S10 2TN, UK

Energies, 2021, vol. 14, issue 20, 1-18

Abstract: Current distribution anomaly can be used to indicate the onset of package-related failures modes in Silicon Carbide power MOSFET modules. In this paper, we propose to obtain the wire bond’s magnetic field profile using an array of Tunnel Magneto-Resistance (TMR) sensors, and characterise the small changes in the current density distribution to find the onset of the wire bond degradation processes, including wire bond lift-off, wire bond cracking, and wire bond fracture. We propose a novel condition monitoring technique where a non-galvanic high-bandwidth sensing and a reliability model monitor the health of the power switches. We designed a dedicated calibration set-up to examine the sensor array and calibrated to demonstrate the adequate sensitivity to a minimum 5% current anomaly detection in a single wire bond of the switching devices operating with 50 kHz switching frequency. We use a hardware-in-the-loop (HIL) experimental set-up to replicate wire bond-related failures in a 1200 V/55 A SiC MOSFET power module of a DC/DC Boost converter. Signal conditioning circuits are further designed to amplify and buffer the sensor readings. Experimental results showed the proposed technique is able to detect a wide range of package-related failures.

Keywords: condition monitoring; current distribution; failure onset; magnetic field; reliability; silicon carbide; wire bond (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
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