Engineering wavefronts with machine learned structured polarization

TL;DR

This paper introduces a novel method for wavefront shaping by controlling polarization patterns using computational algorithms, including machine learning, offering a simpler alternative to traditional phase modulation techniques.

Contribution

It presents two computational approaches for polarization-based wavefront shaping, extending existing algorithms and integrating machine learning for optimized polarization control.

Findings

Polarization control enables effective wavefront shaping.

Machine learning optimization improves polarization pattern design.

Experimental validation confirms the method's efficiency.

Abstract

Optical approaches for wavefront shaping traditionally rely on phase modulation through holographic techniques. Shaping the phase determines a wave's diffraction and hence its intensity distribution in space. We instead show that shaping the polarization introduces a novel framework that permits the spatial modulation of polarization to control wavefront propagation and resulting amplitude distributions. We develop two distinct computational phase retrieval approaches for calculating the required polarization transformations and experimentally validate these. The first method extends the established Gerchberg-Saxton algorithm, while the second employs machine learning optimization to determine optimal polarization patterns. By implementing both amplitude and polarization control simultaneously using a single polarization mask, our approach significantly reduces system complexity compared to traditional methods. Our experimental results demonstrate the potential of polarization-based wavefront shaping as an efficient alternative to conventional techniques, paving the way for applications in optical manipulation and imaging.