Cart (Loading....) | Create Account
Close category search window

Artificial neural network-based model for estimation of EQE of multi-junction solar cells

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Patra, J.C. ; Sch. of Comput. Eng., Nanyang Technol. Univ., Singapore, Singapore ; Maskell, D.L.

External quantum efficiency (EQE) of multi-junction (MJ) solar cells is an important parameter used to optimize the design parameters of the solar cells and to predict the end-of-life (EOL) in space solar cells. The EQE undergoes drastic variations when the solar cell is bombarded with charged particles, due to their complex interactions with cell materials. Usually, elaborate and extensive experimental setup is needed to measure EQE of a MJ solar cell when it is under the influence of charged particles of different energy levels and fluences. In this paper we propose an artificial neural network (ANN)-based model to estimate EQE of triple-junction InGaP/GaAs/Ge solar cells, for proton energies from 30 keV to 10 MeV with fluences ranging from 1010 to 1014 ion/cm2. Using only a small subset of the measured data (taken from Sato et al. [2]) as training set, we have shown that the ANN-based model can estimate the EQE for the complete range with much lower error than the PC1D model reported by Sato et al. [2]. With extensive simulation results we have shown superior performance of the ANN-based models over PC1D in terms of absolute error, mean square error and correlation coefficient between the measured and estimated EQE, under the influence of a wide range of proton energy energies and fluences.

Published in:

Photovoltaic Specialists Conference (PVSC), 2011 37th IEEE

Date of Conference:

19-24 June 2011

Need Help?

IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.