Predicting the Thickness of Solar Films Based on Neural Networks

In the field of measuring the thickness of solar films, traditional specialized instruments are used to detect solar cell films. However, in this paper, a correlation between color values and solar film thickness is found. Therefore, we consider the use of data fitting method- based on the training data fit to determine the thickness of the film. Two traditional data fitting methods, i.e., ridge and polynomial regression, and RBF neural network methods, were also used in the experiments. The results show that the RBF neural network method outperforms the other two traditional methods in terms of fitting accuracy and significantly reduces the error, with relative errors ranging from 0.22% to 6.11%, providing a guiding Study For Thickness Prediction Of Solar Thin Films. Within acceptable range, the method of neural networks can meet the requirements of actual production. This approach reduces costs compared to using laser measurement.