Search for: All records

Managing the thermal behavior of GaN devices under test (DUT) poses significant challenges during accelerated thermal cycling (ATC) tests, particularly due to the compact packaging of small GaN devices (e.g., QFN package) and the sharp rise in the device's RDSon at high junction temperatures. This paper presents a framework for analyzing and modeling the thermal response performance of the ATC test setup and evaluating the impact of non-linear dissipated power on the GaN DUTs. It outlines the limitations of conventional thermal sensors in accurately estimating the DUT's junction temperature through case temperature measurements under ATC conditions. The analysis and modeling of the experimental junction temperature response function shows about 4 s time constant in the measurements using a thermistor placed near the DUT, highlighting the GaN DUT's susceptibility to thermal runaway under ATC conditions (Tj−max > 125 °C), where the thermal time constant significantly exceeds the DUT's thermal transient time. Consequently, an on-state resistance (RDSon)-based Tj estimation method is employed to monitor the Tj and control the thermal cycling window boundaries effectively. Experimental investigations of several e-mode GaN HEMTs under different ATC windows are conducted to validate the ATC testing framework. Moreover, the temperature coefficient of on-state resistance (α) is characterized and quantified - considering fully packaged individual GaN DUTs’ mechanical and electrical degradation mechanisms. 

DC microgrids incorporate several converters for distributed energy resources connected to different passive and active loads. The complex interactions between the converters and components and their potential failures can significantly affect the grids' resilience and health; hence, they must be continually assessed and monitored. This paper presents a machine learning-assisted prognostic health monitoring (PHM) and diagnosis approach, enabling progressive interactions between the converters at multiple nodes to dynamically examine the grid's (or micro-grid's) health in real time. By measuring the resulting impedance at the power converters' terminals at various grid nodes, a neural network-based classifier helps detect the grid's health condition and identify the potential fault-prone zones, along with the type and location of the fault type in the grid topology. For a faulty grid, a Naive Bayes and a support vector machine (SVM)-based classifiers are used to locate and identify the faulty type, respectively. A separate neural network-based regression model predicts the source power delivered and the loads at different terminals in a healthy grid network. The proposed concepts are supported by detailed analysis and simulation results in a simple four-terminal DC microgrid topology and a standard IEEE 5 Bus system.