TUTORIALS
GAUDENZIO MENEGHESSO
University of Padova, Italy​
He graduated in Electronics Engineering at the University of Padova in 1992 working on the failure mechanism induced by hot-electrons in MESFETs and HEMTs. Since 2011 is with University of Padova as Full Professor. His research interests involve mainly the Electrical characterization, modeling and reliability of microelectronics devices, with particular emphasis on wide bandgap semiconductors. Within these activities he published more than 1000 technical papers (of which more than 100 Invited Papers and 16 best paper awards). Bibliometric indexes (updated August 2024):
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Scopus: Documents: 694, Tot. Citations 16791, h-index: 59
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Google Scholar: Documents: 1300, Tot. Citations 22061, h-index: 69
He has been the Project Coordinator of a European project H2020 – InRel-NPower, and Scientific Coordinator of European project H2020 – GaN4AP - GaN for Advanced Power Applications”. He has been nominated to IEEE Fellow class 2013, with the following citation: “for contributions to the reliability physics of compound semiconductors devices”.
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GaN reliability: from technological considerations to failure processes
​​GaN power devices have excellent properties, making them ideal for power conversion applications. The high sheet channel density, wide bandgap, and large breakdown field of GaN enable significant performance enhancements and loss reductions compared to traditional silicon field-effect transistors. Various device structures are available, including lateral normally-off transistors and devices with fully vertical architecture. This tutorial describes the key properties of gallium nitride material and devices. We present detailed insights into both lateral and vertical device architectures, with the aim of giving a complete understanding of the subject. Then, we address the reliability challenges of GaN devices, particularly focusing on dynamic on-resistance and breakdown processes. Finally, future prospects in the GaN field will be discussed.
Huai WANG
Aalborg University, Denmark ​​
Huai Wang is a Professor at the Department of Energy (AAU Energy), Aalborg University, Denmark. He leads the Reliability of Power Electronic Converters (ReliaPEC) group and chairs the Mission of Digital Transformation and AI at AAU Energy. His research focuses on efficient, reliable, and cognitive power electronic converters. Prof. Wang collaborates with companies across the value chain, from materials and components to systems. He has initiated five short-term industrial/PhD courses, attended by over 800 PhD students and industry engineers in the last decade, and has delivered more than 30 international conference tutorials.Prof. Wang received his PhD from the City University of Hong Kong in 2012 and a B.E. degree from Huazhong University of Science and Technology in 2007. He has conducted short-term research at MIT, ETH Zurich, and ABB Corporate Research Center in Switzerland. He received the IEEE Power Electronics Society's Richard M. Bass Outstanding Young Power Electronics Engineer Award in 2016 and the IEEE Transactions on Power Electronics 1st Prize paper award in 2021. He was elected as a member of the Danish Academy of Technical Sciences in 2023.
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AI-assisted reliability testing, modeling, and condition monitoring for power electronics applications
​​Power electronic converters are "hidden heroes" in numerous modern energy systems, such as wind turbines, Photovoltaics (PV), Power-to-X, electric vehicles, data centers, mobile phones, and smart homes. In contrast to CPUs/GPUs that process digitalized information, power electronic converters process electrical energy by efficiently converting voltage, current, or frequency. They are ubiquitous in electricity generation, transmission, distribution, and consumption, forming a critical part of the infrastructure for the green transition. With an increasing percentage of electricity processed by power electronics technologies, optimizing the efficiency and reliability of converters is critical to affordable, secure, and sustainable energy systems. Artificial Intelligence (AI) is increasingly solving optimization, regression, and classification problems within the energy sector, where deep electrification and digitalization intersect. This tutorial focuses on the application of AI in the power electronics reliability aspect research, including AI-assisted reliability testing for testing time reduction, fast dynamic thermal modeling, and condition monitoring for predictive maintenance. A few case studies will be introduced to demonstrate how AI can assist in addressing the challenges in power electronics reliability research.