Single‐Shot Full‐Stokes Analysis of Partially‐Polarized Light With a Photonic Deep Random Neural Network

ABSTRACT Optical neural networks are emerging as a powerful and versatile tool for processing optical signals directly in the optical domain with superior speed, integrability, and functionality. Their application in polarimetry enables neuromorphic polarization sensors. However, their operation is limited to fully‐polarized light. Here, we demonstrate single‐shot full‐Stokes analysis of partially‐polarized beams with a photonic random neural network (PRNN). The PRNN is composed of a series of optical random layers implemented by a stack of scattering media and a few trainable digital nodes. The setup infers the degree‐of‐polarization and the Stokes parameters of the polarized component at multiple wavelengths with precision comparable to off‐the‐shelf polarimeters. The use of several scattering layers allows to enhance the accuracy, reduce the sensor size, and minimize digital costs, demonstrating the advantage of a deep optical encoder for processing polarization information. Simulations of the encoder as cascaded vector transmission matrices confirm the results. Our work points out photonic neural networks as fast, compact, broadband, low‐cost polarimeters that are widely applicable from sensing to imaging.