Nonorthogonal Polarization‐Multiplexed and Detachable Diffractive Processors

Abstract

ABSTRACT Polarization multiplexing provides an elegant route for parallel all‐optical processing, yet its intrinsic orthogonality fundamentally limits channel capacity and interlayer interaction in diffractive systems. We present a detachable diffractive processor that leverages nonorthogonal polarization multiplexing to overcome these bottlenecks. The processor features a convolutional‐like architecture composed of detachable diffractive layers, enabling global and interlayer function multiplexing with accelerated training and expanded versatility. Experimentally, we realize 12 independent functions—4 classifiers and 8 holograms—across 4 polarization‐decoupled channels in a dual‐layer system and extend to 18 channels supporting 6 classifiers and 36 holograms using a triple‐layer configuration. Furthermore, we demonstrate a multi‐level optical encryption framework that combines image transformation and holographic generation. This work establishes a scalable framework for multifunctional diffractive computing, enabling compact processors, high‐capacity holography, and secure optical information technologies.