Fraunhofer IISB at PCIM Europe in Nuremberg, May 6-8, 2025
Wide-Bandgap (WBG) Semiconductors and Power Electronics
Every year at PCIM Europe, Fraunhofer IISB presents its complete portfolio of research activities along the value chain for power electronics. The spectrum ranges from basic materials, semiconductor devices and process technologies, power electronic modules and components to complete power electronic and energy systems. This year's focus was on aviation electronics. Join us for a retrospective of our exhibition booth and conference activities.
The biggest booth we ever had!
You did not have the opportunity to take a look around our PCIM booth? Then we invite you to take a quick tour of our exhibition by watching this video! Fraunhofer IISB's director Prof. Jörg Schulze as well as heads of departments and group managers present the wide range of our activities.

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When it comes to the power electronics value chain, we have it all:
from semiconductors materials and special devices, processing services, highly reliable power electronic modules and converters to complete power electronic and energy systems. At PCIM, we showcased main target applications like semiconductor manufacturing, electromobility, aerospace as well as sustainable energy supply.
PCIM Conference Young Researcher Award 2025 goes to Lena Köhler from Fraunhofer IISB!
Traditionally, we are not only showcasing at the PCIM exhibition, but also delivering scientific presentations at the PCIM Conference.
We are really proud to announce that our colleague Lena Köhler from the Data Analytics Group has been honored for her work on cognitive power electronics. The award was presented by Prof. Leo Lorenz, president of ECPE.
In her paper "Machine Learning and Digital Twins for RUL Prediction of DC Semiconductor Circuit Breakers", Lena presents a novel approach and base implementation of a digital twin framework to predict remaining useful life (RUL) of power semiconductors in real-time. The prognosis is based on actual device conditions in addition to estimations derived from power cycling tests and numerical degradation models, using machine learning methods.
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