PV for AI, AI for PV – Part 3 of 4
In Part 2, we explored how AI can accelerate the discovery of better photovoltaic materials and cell structures. The next challenge is industrial: manufacturing those cells consistently, affordably and at scale, while preserving their long-term reliability.
A better solar cell only delivers value when we can manufacture it reliably, millions of times.
The sharp decline in PV prices together with the acceleration in electricity demand have provided a formidable support for PV manufacturing. Yet for manufacturers, reducing defects, material consumption and downtime becomes essential to competitiveness.
PV factories are already highly automated. Photovoltaic Manufacturing 4.0 takes this further by connecting robotics, AI and machine learning with cyber-physical systems like production equipment linked to sensors, software and digital models. The opportunity is to make inspection, process control and production decisions work together. Fraunhofer CSP’s MK4 platform illustrates this approach, combining measurement, data analysis and adaptive control under near-production conditions.
Consider three practical opportunities.
Detect defects earlier and learn why they happen.
AI can analyse inspection images to identify cracks, printing defects and electrical anomalies. Connecting these observations with each cell’s production history can help engineers trace recurring problems to particular tools, recipes or material batches. Earlier intervention can prevent further processing of defective products and reduce repeated losses. Europe’s SHINE PV project brings this ambition into manufacturing development. Started in January 2025, it is developing Industry 4.0-enabled equipment for metallisation, post-processing and interconnection, including routes to replace silver partly or entirely with copper. Its target is demonstration in an industrial environment at technology readiness level 7.
Use less material while protecting performance.
Metallisation provides a concrete example. Finer printed contacts can reduce silver consumption and shading, but maintaining electrical quality becomes more demanding. A 2026 Fraunhofer-Institut für Solare Energiesysteme ISE study demonstrated machine learning that predicts contact-finger resistance and identifies defective structures from optical measurements. This offers a practical route to monitoring the quality of increasingly fine contacts during production.
Keep processes stable and equipment productive.
AI models can compare equipment behaviour with expected operating conditions, helping identify process drift and potential component faults. Combined with validated digital models, these tools can support maintenance decisions and help engineers investigate changes before committing them to production.
The foundation is connected, accurate and trustworthy production data. Material batches, machine settings, inspection images and electrical measurements must be reliably linked. This requires calibrated sensors, consistent data definitions and clear governance: who owns the data, checks its quality and controls changes.
Models must be validated against independent measurements under representative production conditions, then monitored as equipment, materials and recipes evolve. Systems should be transparent and auditable, recording the data used, model version, recommendations and resulting actions. Engineers need to understand a recommendation’s basis and uncertainty and retain authority over consequential process changes. Reliable AI depends on disciplined measurement and accountable decisions.
Looking at Awendio Solaris R&D roadmap, a practical application we propose is to connect R&D pilot-line process data with cell inspection and electrical measurements, initially focusing for example on metallisation. Such initiative would target a significant silver consumption reduction while maintaining or improving manufacturing yield and module reliability.
Progress should be measured in fewer rejects, lower cost per saleable watt and consistent durability. Each production run should deepen our understanding of how to achieve those outcomes.
How much more competitive could solar manufacturing become if every cell or module produced helped us understand how to make the next batch better?
Next: how AI can improve solar-system design, operation and integration into the grid.
