DINSDALE, Nicholas; WIECHA, Peter; DELANEY, Matthew; REYNOLDS, Jamie; EBERT, Martin; ZEIMPEKIS, Ioannis; THOMSON, David; REED, Graham; LALANNE, Philippe; VYNCK, Kevin; MUSKENS, Otto

doi:10.1021/acsphotonics.0c01481

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DINSDALE, Nicholas
Optoelectronics Research Centre [Southampton] [ORC]
School of Physics and Astronomy [Southampton]

WIECHA, Peter
School of Physics and Astronomy [Southampton]
Équipe Matériaux et Procédés pour la Nanoélectronique [LAAS-MPN]

DELANEY, Matthew
Optoelectronics Research Centre [Southampton] [ORC]
School of Physics and Astronomy [Southampton]

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ACS photonics. 2021-01, vol. 8, n° 1, p. 283-295

American Chemical Society

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Recent breakthroughs in photonics-based quantum, neuromorphic and analogue processing have pointed outthe need for new schemes for fully programmable nanophotonic devices. Universal optical elements based oninterferometer meshes are underpinning many of these new technologies, however this is achieved at the cost ofan overall footprint that is very large compared to the limited chip real estate, restricting the scalability of thisapproach. Here, we propose an ultracompact platform for low-loss programmable elements using the complextransmission matrix of a multi-port multimode waveguide. Our approach allows the design of arbitrary trans-mission matrices using patterns of weakly scattering perturbations, which is successfully achieved by means of adeep learning inverse network. The demonstrated platform allows control over both the intensity and phase in amultiport device at a four orders reduced device footprint compared to conventional technologies, thus openingthe door for large-scale integrated universal networks< Réduire

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Deep learning enabled design of complex transmission matrices for universal optical components

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