Prediction technique of aberration coefficients of interference fringes and phase diagrams based on convolutional neural network

Abstract

In this study, we present a new way to predict the Zernike coefficients of optical system. We predict the Zernike coefficients through the function of image recognition in the neural network. It can reduce the mathematical operations commonly used in the interferometers and improve the measurement accuracy. We use the phase difference and the interference fringe as the input of the neural network to predict the coefficients respectively and compare the effects of the two models. In this study, python and optical simulation software are used to confirm the overall effect. As a result, all the Root-Mean-Square-Error (RMSE) are less than 0.09, which means that the interference fringes or the phase difference can be directly converted into coefficients. Not only can the calculation steps be reduced, but the overall efficiency can be improved and the calculation time reduced. For example, we could use it to check the performance of camera lenses.