Advanced modelling and characterization for power semiconductor materials and technologies

The proposed project AddMorePower aims to advance X-ray- and electron-probe related characterization techniques to make them quantitative and automated tools for the power semiconductor industry, and to refine modelling (using MODA) and FAIR data-management methods to enhance and efficiently use characterization data (using CHADA). Thereby, AddMorePower will promote the materials integration and development for European power semiconductor technologies, to allow a broader and faster market penetration, while also providing new opportunities for other industries basing themselves on mono- and polycrystalline materials. With the rapid and massive spread of power electronics and power semiconductors to enable the digitalization and the electrification of our society and its supply with sustainable energy, new requirements arise to the conception and integration of semiconductor and interconnect materials. AddMorePower will provide the necessary characterization and modelling techniques that meet the particular needs of the upcoming power semiconductor technology generations:1.The transition to the new semiconductor materials gallium nitride (GaN) and silicon carbide (SiC), mainly limited by defects in the crystal lattice, for which currently no established characterization workflows exist.2.The starting 3D-integration also of power devices, posing severe thermo-mechanical challenges to the involved metals and intermetallic materials, which can only be mastered by understanding gained by predictive modelling.3.The trend towards digitalization and industry4.0, which requires FAIR (findable, accessible, interoperable and reusable) data at all development and production steps.The project brings together renowned research institutes with many years of experience in electron- and X-ray characterization, emerging new research groups and company start-ups and researchers with a track record in multi-physics materials modelling as well as data engineering.