Reliability of Organic Compounds in Microelectronics and Optoelectronics

Willem Dirk van Driel has a >20-year track record in the reliability domain. Application areas range from healthcare, gas and oil explorations, semiconductors and my current position in Philips Lighting where he is responsible for Solid State Lighting reliability. Besides that, he holds a professor position at the University of Delft, The Netherlands. His scientific interests are solid state lighting, microelectronics and microsystems technologies, virtual prototyping, virtual reliability qualification and designing for reliability of microelectronics and microsystems. He acts as the chair for the organizing committee of the IEEE conference EuroSimE and has authored and co-authored more than 300 scientific publications, including journal and conference papers, book or book chapters and invited keynote lectures. He holds 20 patents. He is a certified DFSS Black Belt. In her PhD and Post-Doc projects Dr. Yazdan Mehr has extensively worked on different aspectsof LED-based products, i.e. materials, design, reliability, and degradation. She developed a deep understanding of correlation between design, materials, and degradation mechanism relationships in LEDs. As well, she has a solid background in Materials Engineering as Dr Yazdan Mehr performed her Master at Materials Engineering Department of TUDelft.

• Introduction to reliability.- Introduction to failure analysis methodologies.- An overview of remaining life assessment methodologies in engineering materials.- Reliability and failure of microelectronic materials and components in harsh working conditions.- Reliability and failure of solar cell materials and systems in harsh working conditions.- Electromigration-induced failures in microelectronic components.- Corrosion and degradation of metals.- Creep failures in high temperature alloys.- An overview of failures in boilers and heat exchangers in power plants.- Fatigue-related failures.- Degradation and failure of polymers.- Virtual prototyping techniques for prediction material degradation.- Health monitoring, machine learning and digital twin.-Discussions and concluding remarks.- Index.