Piston Sensing for Sparse Aperture Systems via All-Optical Diffractive Neural Network

It is a crucial issue to realize real-time piston correction in the area of sparse aperture imaging. This paper demonstrates that an optical diffractive neural network is capable of achieving light-speed piston sensing. By using detectable intensity distributions to represent pistons, the proposed method can convert the imaging optical field into estimated pistons without imaging acquisition and electrical processing, thus realizing the piston sensing task all-optically. The simulations verify the feasibility of the approach for fine phasing, with testing accuracy of lambda/40 attained. This method can greatly improve the real-time performance of piston sensing and contribute to the development of sparse aperture system.