Abstract

Enhancing light absorption within thin film amorphous silicon (a-Si) solar cells should lead to higher efficiency. This improvement is typically done using various light trapping techniques such as utilizing textured back reflectors for pronounced light scattering within the cell thus achieving higher absorption. It is believed that embedding metallic nanoparticles (MNPs) inside the structure could increase light scattering. However, embedding MNPs can also cause significant structure defects and pronounced efficiency drop as well – it has been indicated by many experiments that disproved this belief. In search of ways to improve efficiency, we have investigated the impact of MNPs size, and location within the solar cell, in addition to the effect of defects, and doping levels on the overall efficiency. On the basis of our 3D multiphysics (optical-electric) modeling, we developed a design guideline for embedding these MNPs and reducing the impact of defects created in the embedding process. The results of simulations were compared to relevant measured data, and it showed a good agreement. Subsequently, models were used to predict performance, and over 30% improvement in solar cell efficiency (~13% is predicted), which is beyond the state of the art. This was predicted by optimizing the size and location of the MNPs and tailoring the doping levels to have better forward light trapping and absorption.

Abstract

It is well known that the performance of solar cells may significantly suffer from local electric defects. Accordingly, infrared thermography (i.p. lock-in thermography) has been intensely applied to identify such defects as hot spots. As an imaging method, this is a fast way of module characterization. However, imaging leads to a huge amount of data, which needs to be investigated. An automatized image analysis would be a very beneficial tool but has not been suggested so far for lock-in thermography images. In this manuscript, we describe such an automatized analysis of solar cells. We first established a robust algorithm for segmentation (or recognition) for both, the PV-module and the defects (hot spots). With this information, we then calculated a parameter from the IR-images, which could be well correlated with the maximal power (Pmpp) of the modules. The proposed automatized method serves as a very useful foundation for faster and more thorough analyses of IR-images and stimulates the further development of quality control on solar modules.