Real-time wavefront correction using diffractive optical networks

Real-time wavefront correction is a challenging problem to present for conventional adaptive optics systems. Here, we present an all-optical system to realize real-time wavefront correction. Using deep learning, the system, which contains only multiple transmissive diffractive layers, is trained to realize high-quality imaging for unknown, random, distorted wavefronts. Once physically fabricated, this passive optical system is physically positioned between the imaging lens and the image plane to all-optically correct unknown, new wavefronts whose wavefront errors are within the training range. Simulated experiments showed that the system designed for the on-axis field of view increases the average imaging Strehl Ratio from 0.32 to 0.94, and the other system intended for multiple fields of view increases the resolvable probability of binary stars from 30.5% to 69.5%. Results suggested that DAOS performed well when performing wavefront correction at the speed of light. The solution of real-time wavefront correction can be applied to other wavelengths and has great application potential in astronomical observation, laser communication, and other fields.

Automated summary

We present an all-optical system using deep learning to achieve real-time wavefront correction for unknown, random, and distorted wavefronts, with high-quality imaging results. The system, consisting of multiple transmissive diffractive layers, is physically placed between the imaging lens and the image plane for all-optical correction of unknown wavefronts within the training range. Simulated experiments showed significant improvements in imaging Strehl ratio and resolvable probability of binary stars. The solution of real-time wavefront correction has potential applications in astronomy, laser communication, etc.