Predicting Loss Analysis from Luminescence Images in Si Solar Cells with Convolutional Neural Networks

Abstract

In this work, deep convolutional neural networks (CNNs) are used to speed up the calculation of spatial nonuniform device parameters, voltage and fill factor (FF) losses in crystalline Si solar cells. Luminescence images on finished solar cells, specifically as-measured electroluminescence (EL) and photoluminescence (PL) images, are used as input features to train the CNN models and losses analysis as output is from a finite-element modeling program called Griddler. From the analysis of a small dataset of 250 commercial grade solar cells, the CNN models are able to predict the spatially nonuniform distribution of contact resistance and defect parameters of the devices under test and exhibit good performance in the inference of voltage and FF losses. As EL and PL images are widely collected in-line production data, in the result, the possibility of deploying 2D spatial power loss analysis is implied as a fast, in-line, and nondestructive analytic tool for solar cells manufacturing.