Uniform design and deep learning based liquid lens optimization strategy toward improving dynamic optical performance and lowering driving force

Abstract

An efficient optimization strategy for liquid lens combining the uniform design and the deep learning is proposed to achieve improved dynamic optical performance and lowering driving force simultaneously. The membrane of the liquid lens is designed into a plano-convex cross-section, in which the contour function of the convex surface as well as the central membrane thickness is especially optimized. The uniform design method is initially utilized to select a part of uniformly distributed and representative parameter combinations from all possible parameter range, and their performance data is then obtained through simulation using MATLAB to control COMSOL and ZEMAX. After that, a deep learning framework is employed to build a four-layer neural network with its input and output layer representing the parameter combinations and the performance data, respectively. After 5 × 103 epochs, the deep neural network has undergone sufficient training, demonstrating effective performance prediction capability for all parameter combinations. Finally, a “globally” optimized design can be obtained by setting appropriate evaluation criteria which take the spherical aberration, the coma and the driving force into consideration. Compared with the conventional design using uniform membrane thickness of 100 µm and 150 µm as well as the previously reported “locally” optimized design, distinct improvements in the spherical and the coma aberrations across the entire focal length tuning range have been achieved, whilst the required driving force is largely reduced. In addition, the “globally” optimized design exhibits the best modulation transfer function (MTF) curves and provides the best image quality.