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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Patra, J.C. Modanese, C. Acciarri, M. |
| Copyright Year | 2015 |
| Description | Author affiliation: Dept. of Mater. Sci. & Eng., Norwegian Univ. of Sci. & Technol., Trondheim, Norway (Modanese, C.) || Dept. of Mater. Sci., Univ. of Milano Bicocca, Milan, Italy (Acciarri, M.) || Fac. of Sci., Eng. & Technol., Swinburne Univ. of Technol., Melbourne, VIC, Australia (Patra, J.C.) |
| Abstract | Because of economic and energy-consumption considerations, multicrystalline solar grade silicon (mc-SoG-Si), instead of expensive electronic-grade Si, is being considered in photovoltaic (PV) 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 three main electronic parameters, e.g., majority carrier mobility (μ), majority carrier density (p) and resistivity (ρ), of compensated mc-SoG-Si vary nonlinearly with temperature due to several complex mechanisms. In this paper, we propose two artificial neural network (ANN)-based models to predict these electronic parameters of mc-SoG-Si material. Using a limited amount of measurement data, we have shown that the first ANN-based model can predict the three electronic parameters of a given sample without accounting for the compensation ratio over a wide temperature range of 70-400 K. Whereas, the second ANN model can predict these electronic parameters of a given sample with unknown compensation ratio over the same temperature range. With extensive simulation results we have shown that these models can predict the three parameters with a maximum error of ±10%. |
| Starting Page | 1 |
| Ending Page | 8 |
| File Size | 4199391 |
| Page Count | 8 |
| File Format | |
| ISSN | 21614407 |
| e-ISBN | 9781479919604 |
| DOI | 10.1109/IJCNN.2015.7280426 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2015-07-12 |
| Publisher Place | Ireland |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Temperature measurement Energy measurement Measurement uncertainty Scattering Crystallization Standards Oxygen prediction of electronic parameters Artificial neural network model compensated multicrystalline SoG silicon |
| Content Type | Text |
| Resource Type | Article |
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