Phase Multiplexed Optical Computing: Reconfiguring a Multi‐Task Diffractive Optical Processor Using Illumination Phase Diversity
Year: 2026
Type: article
Abstract: ABSTRACT Optical computing based on diffractive networks attracted broad attention due to its low latency, low energy consumption, and parallelism. Given sufficient degrees‐of‐freedom, a diffractive network composed of spatially‐optimized surfaces can all‐optically implement an arbitrary complex‐valued linear transformation between an input and an output aperture. Here, we report a monochrome multi‐task diffractive network architecture that leverages illumination phase multiplexing to dynamically reconfigure its output function and implement a large set of complex‐valued linear transformations. Each of the T target transformations is encoded and addressed by a unique 2D illumination phase profile (“phase key”) applied to the input aperture, activating the corresponding transformation at the output field‐of‐view. A common diffractive network, jointly optimized with T phase keys, demultiplexes these encoded inputs and executes any of the T transformations. We show that a network with optimized diffractive features can accurately realize T distinct complex‐valued linear transformations for arbitrary complex input fields, where and refer to the input/output pixels, respectively. In a proof‐of‐concept numerical study, T = 512 transformations were implemented with negligible error. Compared with wavelength multiplexing, phase multiplexing yields lower transformation errors, enabling larger‐scale monochrome optical processors for computing and machine vision.
Source: Laser & Photonics Review
DOI: 10.1002/lpor.71487
Authors: Xiao Wang, Aydogan Ozcan
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $4,880
OpenAlex ID: W7167056113