Prediction of Second-Harmonic Generation Wave-Front Distribution by Extreme Learning Machine

Abstract

In the applications of wave-front detection using second-harmonic generation, the spatial phase distribution needs to calculate accurately before and after frequency doubling in real-time. This letter presents a learning-based method called extreme learning machine to fit the corresponding relationship of phase between the fundamental frequency wave and the second-harmonic. The Zernike coefficients of the fundamental frequency wave wave-front and the second-harmonic wave-front are used as input data for Extreme Learning Machine model training and testing. The effects of the intensity-dependent phase shift and walk-off are also considered. The reliability of the trained Extreme Learning Machine model was accessed based on simulation results. The proposed method has shown distinct competitive advantages in real-time calculation efficiency. The well-trained Extreme Learning Machine model only needs 0.026 seconds to accurately predict the phase distribution of the fundamental frequency wave. The runtime is three orders of magnitude smaller than the traditional numerical calculation method.