SOLAR-SKIES: AI-supported optimization of CIGS thin-film PV technology

Challenge: Increasing Efficiency and Reducing Costs through Data-Driven Approaches

Photovoltaic (PV) technologies are a key component of the energy transition but face challenges related to efficiency, cost, and environmental compatibility. Flexible CIGS thin-film solar cells offer tremendous potential but require continuous optimization of materials and processes. SOLAR-SKIES addresses these challenges through data-driven approaches and AI-based modeling.

The SOLAR-SKIES project focuses on three main objectives:

• Increasing PV Efficiency: AI-based modeling and data analysis aim to significantly enhance the conversion efficiency of flexible CIGS PV cells. The goal is 18% efficiency for 1 cm² solar cells and 14% for 10×10 cm² modules.
• Reducing Manufacturing Costs: By analyzing extensive process data and using automated optimization through AI, production costs of flexible CIGS PV cells are targeted to be reduced to less than €0.9/Wp.
• Sustainability and Stability: Data-driven material analysis enables the selection of eco-friendly, easily recyclable materials. The goal is to ensure performance stability with less than 10% power loss after 1,000 hours at 85% relative humidity and 85°C.

Technological Solution: AI-Powered Analysis and High-Throughput Screening

SOLAR-SKIES combines high-throughput screening with AI-powered analysis to accelerate and optimize the development process of thin-film solar cells. Data from combinatorial thin-film depositions and automated measurement techniques are collected and analyzed, with AI models identifying optimal material combinations and process parameters.

Using machine learning and Bayesian networks, correlations between process parameters, material properties, and solar cell performance are determined. These data-driven insights enable precise control of manufacturing processes and the prediction of new material combinations and process parameters. This accelerates development and helps evaluate and scale innovative materials more quickly.

Identified optimizations are scaled from the lab to pilot scale without requiring costly production line modifications. This approach facilitates cost-effective and sustainable production scaling.

RISC Software GmbH: Expertise in AI-Based Methods

RISC Software GmbH contributes to the project by applying AI-based methods to identify causal dependencies between process parameters in production, material properties, and the efficiency of produced solar cells.

The underlying measurement data for these analyses are collected through high-throughput screening. RISC Software GmbH also supports data collection and efficient data management, enabling the upscaling of research results to industrial processes.

This project is funded by the FFG.