Estimation of external quantum efficiency for multi-junction solar cells under influence of charged particles using artificial neural networks

External quantum efficiency (EQE) of a solar cell is an important parameter as it determines the design efficiency and overall conversion efficiency. The EQE of solar cells for space applications is adversely affected due to bombardment of charged particles in space. Numerical model based softwares, e.g., PC1D, can be used to estimate the EQE under such situation. By varying the cell parameters to fit the measured EQE one can obtain degradation performance of space solar cells. However, due to complex phenomena and interactions occurring between the junctions of the solar cells and the nonlinear influence of charged particles, the accuracy of these models may be limited. In this paper we propose an artificial neural network (ANN)-based model to estimate the EQE performance of triple junction InGaP/GaAs/Ge solar cells under the influence of wide range of charged particles. With extensive simulation results we have shown that the ANN-based models provide better estimate of the EQE in terms of mean square error and correlation coefficient than the results reported by Sato et al.