Characterisation, Defects & Machine Learning

Making the most energy conversion efficient solar cell requires careful inspection of the materials used to make them and of the fabrication processes along the entire production chain. Identifying the causes of efficiency losses and developing ways to mitigate or eliminate them is the work our group focuses on, and SPREE is a world-leader in this kind of characterisation and inspection of solar PV devices.

A special focus of our group is photoluminescence imaging. This technique was developed at UNSW in 2005 by Professor Trupke and Dr Bardos. In the last decade, photoluminescence imaging has become a standard characterisation process in the photovoltaic industry, utilised by almost every silicon solar cell manufacturer in the world. Our group is also very active in the characterisation of defects in photovoltaic devices (silicon and non-silicon). Recently we started a new research project aiming to develop machine learning techniques for photovoltaic applications. We are using our extensive expertise in interpreting correlations between various metrics and solar cell performance to establish a new research area that combines photovoltaics and computer science.

Our research spans a wide variety of new applications for photovoltaics, such agri-photovoltaics (Agro-PV), building-integrated photovoltaics (BIPV) and Internet of Things (IoT).

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