How APECS leads the way in heterogeneous integration by providing diverse technologies on a single platform including failure analysis and material diagnostics for microelectronics

Under the EU Chips Act, the APECS (Advanced Packaging and Integration for Electronic Components and Systems) pilot line will drive Europe’s technological resilience and strategic autonomy. Coordinated by the Fraunhofer-Gesellschaft and implemented by the Research Fab Microelectronics Germany (FMD), APECS integrates expertise from ten partners across eight European countries. This comprehensive initiative supports large enterprises, SMEs, and start-ups by providing seamless design-to-production capabilities and enabling scalable manufacturing solutions. Through System Technology Co-Optimization (STCO), APECS advances heterogeneous system integration, essential for sectors such as telecommunications, AI/ML, high-performance computing, medical instrumentation, and industrial manufacturing. APECS strengthens Europe’s semiconductor supply chain, reduces reliance on foreign suppliers, and aligns with the European Green Deal’s sustainability goals. By enhancing Europe’s innovative capacity, APECS ensures a robust foundation for cutting-edge technology and critical applications across diverse industries.

Already very early in the development history of microelectronics components, researchers ran into problems with ‘stress’. Actually, one could have expected this: When putting different materials together, with different thermal expansion coefficients or lattice distances, and subjecting them to high temperature steps, stress is bound to pop-up. And too high stress easily results in damage. However, mechanical stress is not always bad. It affects important material properties such as the mobility of charge carriers and helps MEMS to stay straight. As such, it can be turned into something positive. Indeed, also microelectronics devices, in some cases, work better under stress.
This lecture will present the never-ending story of stress in microelectronics and show how stress was measured and modeled, and, depending on the situation, solved, used or circumvented. It is a story of the good, the bad and the ugly.

Micro-Electro-Mechanical Systems (MEMS) have revolutionized the semiconductor industry by seamlessly integrating electronics and mechanics to drive innovation in sensing and actuation at the microscopic scale. In a landscape shaped by the rapid evolution of artificial intelligence (AI), the topics of energy efficiency and sustainability have become increasingly crucial. MEMS emerge as key enablers of sustainability, offering precise sensing capabilities and enhanced energy efficiency. The keynote presentation explores the transformative potential of MEMS in reshaping our daily lives and propelling us towards a greener, more efficient future.

Power Semiconductors based on Gallium-Nitride (GaN) will provide a major contribution to energy and material efficiency in power electronics. At the moment we are privileged to witness the implementation of GaN as game changing semiconductor material for power devices and systems. This keynote will show the most promising Gallium-Nitride based device concepts and major innovations enabling the wide adoption of GaN in a broad range of applications. Important aspects relevant for the industrialization of GaN power devices will be explained. Finally, it will be shown that an outstanding R&D and manufacturing ecosystem is a key success factor in this exciting innovation journey.

Various AI applications in Failure Analysis (FA) have already shown that many routine tasks can be successfully automated. These tasks include identifying physical failures in images, labeling job reports, recommending analysis tasks, and retrieving important textual or visual information. However, most case studies tend to focus on a single application within traditional data science contexts, which typically involve the collection of a dataset, its labeling, model training, and deployment. This approach is effective as long as the number of deployed models remains small and can be managed by a limited group of FA engineers. In reality, an FA lab may deal with a large number of physical faults, often in the hundreds, making the conventional data science approach impractical. This talk will explore potential strategies for integrating AI into the workflows of an FA lab aimed at ensuring the stable and sustainable development and operation of AI components.