Artificial neural network‐based modelling of compensated multi‐crystalline solar‐grade silicon under wide temperature variations

In recent years, multi‐crystalline solar grade silicon (mc‐SoG‐Si), instead of expensive electronic‐grade Si, is being considered in photovoltaic industry for production of solar modules. These materials usually contain a comparable amount of acceptors (e.g. boron) and donors (e.g. phosphorus) and are therefore called compensated mc‐SoG‐Si. The electrical parameters, e.g. majority carrier mobility ( μ ), majority carrier density ( p ) and resistivity ( ρ ), of compensated mc‐SoG‐Si which affect performance of the solar cells vary non‐linearly with temperature due to several complex mechanisms. In this study, the authors propose artificial neural network (ANN)‐based models to predict the three electrical parameters of mc‐SoG‐Si material. Using a limited amount of measurement data, the authors have shown that the ANN‐based models can predict the three electrical parameters of a given sample over a wide temperature range of 70–400 K and a specific range of compensation ratio. The authors have shown with extensive simulated results that these models can predict the three parameters with a maximum error of ±10%.

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