Prognosis of enhance mode gallium nitride high electron mobility transistors using on-state resistance as fault precursor

Gallium Nitride (GaN) high electron mobility transistor (HEMT) is one of the promising candidates to replace existing switches in high-frequency high power converter applications. Reliability of GaN HEMT is an important issue for its commercial deployment. Online prognosis of this transistor ensures robust reliability for mission critical applications. Online prognosis requires identification of fault precursors which shows sensitivity to degradation. Although gate threshold voltage and gate leakage current are identified as fault precursors, Extraction methods of these parameters are offline. In this paper, on-state resistance (R_{DS, ON}) is investigated as an online fault precursor for GaN HEMT. Temperature variation during its operation results in irreversible damage to GaN HEMT. Adaptive thermal network based temperature estimation method using Extended Kalman Filter (EKF) is also proposed. A sequential Monte-Carlo simulation based data driven remaining useful life (RUL) estimation method is also proposed which can be applicable for schedule maintenance before breakdown. Experimental validation of these proposed methods have been presented.