Work

Self-configuring high-speed multi-plane light conversion

Year: 2025

Type: article

Abstract: Multi-plane light converters (MPLCs) - also known as diffractive neural networks - convert an orthogonal set of optical fields into any other orthogonal set via a unitary transformation. MPLC design typically involves optimising a digital model. However, inherently high levels of complexity mean that even a minor mismatch between this model and the physically realised MPLC leads to a severe reduction in performance. Here we create a self-configuring MPLC, converging in minutes while automatically absorbing unknown misalignments and aberrations into the design. To achieve this we introduce 'multi-plane wavefront shaping' - allowing multiple spatial light modes to be reshaped simultaneously. Convergence is accelerated via a high-speed MPLC platform incorporating a kHz-rate phase-only light modulator. Using this approach we demonstrate arbitrary optical transformations and universal mode sorters. Our work paves the way towards ultra-high-fidelity MPLCs with potential applications to optical communications, photonic computing and imaging.

Source: Nature Communications

DOI: 10.1038/s41467-025-66798-2

Authors: José C. A. Rocha, Unė G. Būtaitė, Joel Carpenter, David B. Phillips

Institutions: The University of Queensland, University of Exeter

Language: English

Topic: Random lasers and scattering media

Subfield: Acoustics and Ultrasonics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $6,990

Funders: Research Councils UK, European Commission, Department of Education and Training