Researchers will work with UNSW spinout company BT Imaging to accelerate the commercialisation of solar cell defect detection technology, thanks to a $1.4m commercialisation project.
A breakthrough contactless inspection system developed at UNSW could soon become the new global standard in solar cell testing - cutting waste, doubling production speed and saving the photovoltaic industry an estimated $US1.4 billion a year.
UNSW researchers are taking their game-changing solar cell inspection technology to market, thanks to a $400,000 grant from the Trailblazer Recycling & Clean Energy (TRaCE) Lab to Market Fund and a $1 million contribution by their industry partner, UNSW spinout BT Imaging .
The ACDC (Artificial Intelligence, Characterisation, Defects and Contacts) Research Group at UNSW is partnering with BT Imaging to accelerate the development of contactless technology, which incorporates advanced imaging and machine learning to produce detailed maps of key electrical parameters and defects in solar cells.
The technology is expected to revolutionise solar cell manufacturing worldwide.
Project lead Professor Ziv Hameiri said his team's invention addressed urgent shortcomings in the quality control testing used by solar cell manufacturers.
"While solar cells have advanced dramatically in recent years, with more sophisticated structures and outstanding performance, the main quality inspection tool has remained largely unchanged for over a decade," he said.
"The incumbent 'current-voltage' testers must physically touch the fragile surface of cells, which often leads to damage. The method also struggles with current cell components such as multi-busbars, zero-busbars, and back contacts, as well as next-generation technologies like perovskite and tandem solar cells.
"Additionally, traditional testing methods can only be used in the late stages of cell production (post-metallisation), which means early-stage defects are missed and production is significantly slowed."
A solar cell testing solution for the future
Prof. Hameiri said the current testers were no longer suitable for measuring modern solar cells and faced even greater limitations with emerging technologies.
His team's new system works by shining light onto a solar cell and analysing the faint glow it emits. This glow, or 'luminescence', reveals key electrical properties such as voltage, series resistance and efficiency. Using advanced imaging and machine learning, test data can be converted into detailed maps of defects, performance and predicted lifespan.
The contactless system offers critical advantages over current testing methods: cells remaining intact during testing and defects can be detected earlier in production. Crucially, the technology works with both current silicon cells and emerging perovskite and tandem solar cells, making it a versatile solution for bringing next-generation solar technologies into mass production.
"Through our collaboration with BT Imaging, we aim to reshape the industry by introducing contactless measurements that overcome the limitations of standard current-voltage testers, while offering lower cost, higher throughput and importantly, new insights that will make solar cells even more efficient and reliable," Prof. Hameiri said.
Taking technology from lab to market
Managing Director of BT Imaging, Dr Shubham Duttagupta, said that as a UNSW spinout, his company was built on the strength of academic research, and partnerships like this allowed his team to keep pushing innovation into the marketplace.
"By combining UNSW's cutting-edge innovation with our commercialisation expertise, we're turning laboratory breakthroughs into practical, factory-ready products for both silicon and next-generation solar cells. We are creating inspection systems for manufacturers all around the world that are faster, more reliable, more accurate and future-proofed than ever before," Dr Duttagupta said.
The project team expects their contactless inspection method to become the new standard across silicon and tandem solar cell production lines, ensuring consistency and reliability at scale. As machine learning capabilities are integrated, from automated defect classification to lifespan prediction, the system will grow even smarter, providing manufacturers with deeper insights and greater confidence in every cell produced.
The demand for solar is reaching new heights, with the International Energy Agency predicting that solar photovoltaic (PV) generation will account for 80% of the growth in global renewable capacity by 2030.
