Open AccessPrediction technique of aberration coefficients of interference fringes and phase diagrams based on convolutional neural network
Author(s) -
Allen Jong-Woei Whang,
Yi-Yung Chen,
CheChen Chang,
Yucheng Liang,
Tsai-Hsien Yang,
Cheng-Tse Lin,
Cheng-Fu Chou
Publication year - 2020
Publication title -
optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.394
H-Index - 271
ISSN - 1094-4087
DOI - 10.1364/oe.402850
Subject(s) - zernike polynomials , optics , mean squared error , artificial neural network , interference (communication) , computer science , astronomical interferometer , interferometry , interference microscopy , algorithm , convolutional neural network , root mean square , phase (matter) , optical aberration , physics , artificial intelligence , mathematics , wavefront , statistics , channel (broadcasting) , quantum mechanics , computer network
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.