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Self-interference defocusing multichannel diffractive optical element for neural-networks-assisted aberration recognition
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DOI：10.1016/j.optlaseng.2025.109551.png)
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Abstract
En 中文
Aberration control is in demand in various applications, including vision correction, improvement of imaging systems of mobile devices, optical microscopes and telescopes, optical systems for remote sensing of the Earth, and information transmission in free space. However, existing interference methods face limitations: classical approaches suffer vibration sensitivity and constrained reference beam choices; Shack-Hartmann sensors provide indirect measurement while Zernike phase contrast and correlation methods detect only weak aberrations; and adaptive techniques converge to local minima via iterative single-point point-spread-function analysis. In this work we propose self-interference, a highly sensitive vibration-resistant technique, which employs single-arm interferometric setup and allows to determine phase aberrations. To form a set of interferograms in one plane, a new class of multichannel self-referential DOEs with a tuned focus is developed, which, together with a trained neural network, it allows recording aberrations. Determination of phase distortions of the wavefront is achieved by training a convolutional neural network on 2304 synthesized patterns. To verify the calculated optical element combining the reference and the studied beams, an experiment was performed using a spatial light modulator. In the experimental implementation we studied, the DOE formed 25 self-referential orders. The main result is high-precision recognition of wavefront distortions with an absolute error of 0.0055 for model interferograms. The proposed interference technique can be used in real time using a trained neural network and is applicable to such problems as optical wireless transmission of information under turbulence, design and manufacture of optical elements.
Keywords:
Wavefront distortion
Interferometry
Self-interference
Machine learning
Journal
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