Characterization and Optimization of the Contact Formation for High-Performance Silicon Solar Cells

  • Authors:

  • Sungjoon Lee;A. Pandey;Dongseop Kim;A. Rohatgi;Gary S. May;Sangjeen Hong;Seungsoo Han

  • Affiliations:

  • Myongji University, Department of Information Engineering, & Myongji IT Engineering Research Institute (MITERI), Yongin, Kyunggi 449-728, Korea;Georgia Institute of Technology, School of Electrical Engineering, Atlanta, GA 30332, USA;Georgia Institute of Technology, School of Electrical Engineering, Atlanta, GA 30332, USA;Georgia Institute of Technology, School of Electrical Engineering, Atlanta, GA 30332, USA;Georgia Institute of Technology, School of Electrical Engineering, Atlanta, GA 30332, USA;Myongji University, Department of Information Engineering, & Myongji IT Engineering Research Institute (MITERI), Yongin, Kyunggi 449-728, Korea;Myongji University, Department of Information Engineering, & Myongji IT Engineering Research Institute (MITERI), Yongin, Kyunggi 449-728, Korea

  • Venue:
  • ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks, Part III
  • Year:
  • 2007

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Abstract

In this paper, p-n junction formation using screen-printed metallization and co-firing is used to fabricate high-efficiency solar cells on single-crystalline (SC) silicon substrates. In order to form high-quality contacts, co-firing of a screen-printed Ag grid on the front and Al on the back surface field is implemented. These contacts require low contact resistance, high conductivity, and good adhesion to achieve high efficiency. Before co-firing, a statistically designed experiment is conducted. After the experiment, a neural network (NN) trained by the error back-propagation algorithm is employed to model the crucial relationships between several input factors and solar cell efficiency. The trained NN model is also used to optimize the beltline furnace process through genetic algorithms.