Quantitative Analysis of Perovskite Morphologies Employing Deep Learning Framework Enables Accurate Solar Cell Performance Prediction

Abstract

In perovskite solar cells, grain boundaries are considered one of the major structural defect sites, and consequently affect solar cell performance. Therefore, a precise edge detection of perovskite grains may enable to predict resulting solar cell performance. Herein, a deep learning model, Self-UNet, is developed to extract and quantify morphological information such as grain boundary length (GBL), the number of grains (NG), and average grain surface area (AGSA) from scanning elecron microscope (SEM) images. The Self-UNet excels conventional Canny and UNet models in edge extraction; the Dice coefficient and F1-score exhibit as high as 91.22% and 93.58%, respectively. The high edge detection accuracy of Self-UNet allows for not only identifying tiny grains stuck between relatively large grains, but also distinguishing actual grain boundaries from grooves on grain surface from low quality SEM images, avoiding under- or over-estimation of grain information. Moreover, the gradient boosted decision tree (GBDT) regression integrated to the Self-UNet exhibits high accuracy in predicting solar cell efficiency with relative errors of less than 10% compared to the experimentally measured efficiencies, which is corroborated by results from the literature and the experiments. Additionally, the GBL can be verified in multiple ways as a new morphological feature.