Universal Polarization Transformations: Spatial Programming of Polarization Scattering Matrices Using A Deep Learning-Designed Diffractive Polarization Transformer.

Spatial manipulation of polarization, with the controlled synthesis of optical fields having non-uniform polarization distributions, presents a challenging task. Here, we demonstrate universal polarization transformers based on an engineered diffractive volume, which can synthesize a large set of arbitrarily-selected, complex-valued polarization scattering matrices between the polarization states at different positions within its input and output field-of-views (FOVs). This framework comprises 2D arrays of linear polarizers with diverse angles, which are positioned between isotropic diffractive layers, each containing tens of thousands of diffractive features with optimizable transmission coefficients. We demonstrate that, after its deep learning-based training, this diffractive polarization transformer could successfully implement N i  N o = 10,000 different spatially-encoded polarization scattering matrices with negligible error within a single diffractive volume, where N i and N o represent the number of pixels in the input and output FOVs, respectively. We experimentally validated this universal polarization transformation framework in the terahertz part of the spectrum by fabricating wire-grid polarizers and integrating them with 3D-printed diffractive layers to form a physical polarization transformer operating at λ = 0.75 mm wavelength. Through this set-up, we demonstrated an all-optical polarization permutation operation of spatially-varying polarization fields, and simultaneously implemented distinct spatially-encoded polarization scattering matrices between the input and output FOVs of a compact diffractive processor that axially spans 200×λ. This framework opens up new avenues for developing novel optical devices for universal polarization control, and may find various applications in, e.g., remote sensing, medical imaging, security, material inspection and machine vision. This article is protected by copyright. All rights reserved.