Prediction of Electronic Parameters of Compensated Multi-crystalline Solar-grade Silicon using Artificial Neural Networks

Jagdish C. Patra*, Chiara Modanese, Maurizio Acciarri

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

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 (11), majority carrier density (P) and resistivity (p), 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%.

Original languageEnglish
Title of host publication2015 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN)
PublisherIEEE
Number of pages8
DOIs
Publication statusPublished - 2015
MoE publication typeA4 Article in a conference publication
EventInternational Joint Conference on Neural Networks - Killarney, Ireland
Duration: 12 Jul 201517 Jul 2015

Publication series

NameIEEE International Joint Conference on Neural Networks (IJCNN)
PublisherIEEE
ISSN (Print)2161-4393

Conference

ConferenceInternational Joint Conference on Neural Networks
Abbreviated titleIJCNN
CountryIreland
CityKillarney
Period12/07/201517/07/2015

Keywords

  • Artificial neural network model
  • compensated multicrystalline SoG silicon
  • prediction of electronic parameters
  • MOBILITY
  • CELLS
  • MODEL

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