Geometric Parameter Estimations of Perovskite Solar Cells Based on Optical Simulations
This provides a non-invasive method for optimizing perovskite solar cell manufacturing, though it is incremental as it builds on existing neural network techniques.
The paper tackled the problem of non-invasively estimating layer thicknesses in perovskite solar cells by using a convolutional neural network trained on external quantum efficiency data, achieving high accuracy with Bayesian optimization.
This paper presents a non-invasive approach to estimate the layer thicknesses of perovskite solar cells. The thicknesses are predicted by a convolutional neural network that leverages the external quantum efficiency of a perovskite solar cell. The network is trained in thickness ranges where the optical properties are constant, and these ranges set the constraints for the network's application. Due to light sensitivity issues with opaque perovskites, the convolutional neural network showed better performance with transparent perovskites. To optimize the performance and reduce the root mean square error, we tried different sampling methods, image specifications, and Bayesian optimization for hyperparameter tuning. While sampling methods showed marginal improvement, implementing Bayesian optimization demonstrated high accuracy. Other minor optimization attempts include experimenting with input specifications and pre-processing approaches. The results confirm the feasibility, efficiency, and effectiveness of a convolution neural network for predicting perovskite solar cells' layer thicknesses based on controlled experiments.