Intelligent Digitization and Data Concepts for Process Control

With the increasing volume of data in GW solar cell factories, the challenge of efficiently collecting, structuring, and evaluating production data is growing. Quality assurance through automated measurement processes and the storage and visualization of large amounts of data are key requirements. Light-induced degradation (LID), non-STC-compliant measurement conditions, and the complexity of modern cell technologies such as HJT require new approaches to data analysis and measurement data management. At the same time, there is a need to improve the interfaces between data analysis algorithms and MES databases and to integrate AI-supported methods for correlating image and performance data.

The ID² joint project aims to fundamentally improve data lifecycle management in PV production. The focus is on scalable concepts for data structuring, automated quality control, and the development of predictive models for process optimization. Kontron AIS GmbH is developing a framework for the more effective use of production data, including enrichment with quality information to create robust data products.

The ID²-InMetro subproject at Fraunhofer CSP focuses on providing a modular lab-to-fab measurement platform for the latest generation of solar cell technology, enabling cost-effective qualification of measurement equipment in a pre-production environment. In addition, non-STC measurement methods and material-related data analysis tools are being researched, supported by LED solar simulators. Another focus is on ensuring measurement data quality through correlation analyses and cross-comparisons between parallel measurement tools.

Anhalt University of Applied Sciences complements the project by researching fundamental metrological and materials science issues. The aim is to develop new test methods for LeTID and to create specific test benches and test recipes for the rapid evaluation of individual degradation mechanisms.