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Physics-Informed Machine Learning for Rapid Characterisation of Semiconductors

CORDIS · observation · Publication date unknown

Physics-Informed Machine Learning for Rapid Characterisation of Semiconductors The development of more efficient and stable semiconductor energy generation devices, such as photovoltaics (PVs), is key to achieve the EU’s 2050 decarbonisation targets. Tandem architectures, which stack multiple semiconductor junctions, are a promising technology that can enable more efficient and longer-lasting optoelectronic devices that far surpass those of single junction architectures. However, optimizing such complex multi-junction systems requires navigating a vast material and deposition parameter space that cannot be addressed through trial-and-error or conventional modeling alone. MatLearn introduces a novel physics-informed digital twin framework that combines drift-diffusion simulations with machine learning. By embedding semiconductor physics into Physics-Informed Neural Networks (PINNs), we aim to achieve phys

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recordType

award

status

SIGNED

region

EU

value

260347.92

unit

EUR