Projects

The goal of the AziTrim project is the further development and optimization of a process chain for the production of variably oriented lattice structures for use in augmented reality (AR) applications.

The SAB project “MIMODI-3D+ - Miniaturized modular digital 3D+ sensor system with optimized packaging for intelligent condition monitoring” can make a significant contribution to improve predictive condition and process monitoring in highly dynamic systems with new approaches in sensor technology, assembly and connection technology as well as data acquisition and processing.

The MultiALD project focuses on water vapor barrier and crystalline actuator materials based on aluminum-containing thin-film systems. The work includes quantum chemistry-supported process development for layer deposition and characterization.

In the EU project “GENESIS – Generate in Europe a Sustainable Industry for Semiconductor,” Fraunhofer ENAS is making chip production more sustainable. Our subproject develops digital twins for Chemical-Mechanical Planarization (CMP) to significantly reduce resource consumption and emissions – through less slurry, fewer pilot wafers, and lower energy use.

NOVO is a project founded by the European Innovation Commission that aims at making real-time dose verification possible during treatment in a non-invasive way. This is achieved by detecting secondary radiations produced by the proton beam within the patient’s body, which can be measured through a newly designed detector and correlated with the actual proton range. As part of the development plan, it will be necessary to design new reconstruction algorithms to map the detector measurements to the delivered dose as well as tools to allow changes in real time to the treatment plan.

Hybrid AI process models make microelectronics manufacturing more efficient, robust, and sustainable. We link domain-specific process knowledge from simulations and established empirical models with data-driven machine learning methods – optimized for real factory environments with small, heterogeneous datasets. The goal is to reduce energy and resource usage, shorten development times, and strategically strengthen innovation capacity in Saxony.

In the SenMooVe project, a sensor-based solution is being developed to continuously monitor air quality and occupancy. The collected data will be processed using AI methods to assess current and forecasted safety, and the results will be transmitted to existing IT systems of the public transport partners for communication to passengers. This significantly improved data situation will create a real-time data-based decision-making foundation for public transport users, companies, and authorities, ultimately enhancing safety in public transport. Additionally, adaptive control of ventilation and air conditioning can improve sustainability in public transport.