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

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

Recent breakthroughs in photonics-based quantum, neuromorphic, and analogue processing have pointed out the need for new schemes for fully programmable nanophotonic devices. Universal optical elements based on interferometer meshes are underpinning many of these new technologies, however, this is achieved at the cost of an overall footprint that is very large compared to the limited chip real estate, restricting the scalability of this approach. Here, we consider an ultracompact platform for low-loss programmable elements using the complex transmission matrix of a multiport multimode waveguide. We propose a deep learning inverse network approach to design arbitrary transmission matrices using patterns of weakly scattering perturbations. The demonstrated technique allows control over both the intensity and the phase in a multiport device at a four orders reduced device footprint compared to conventional technologies, thus, opening the door for large-scale integrated universal networks.

deep learning, inverse design, light scattering, silicon photonics
2330-4022
283-295
Dinsdale, Nicholas J.
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Wiecha, Peter R.
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Delaney, Matthew
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Reynolds, Jamie
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Ebert, Martin
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Zeimpekis, Ioannis
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Thomson, David J.
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Reed, Graham T.
ca08dd60-c072-4d7d-b254-75714d570139
Lalanne, Philippe
2eaa3c11-ba6e-4de9-85d1-a3788f611964
Vynck, Kevin
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Muskens, Otto L.
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Dinsdale, Nicholas J.
4ec4aa55-6cce-43f7-9c35-cf8c16e8cf86
Wiecha, Peter R.
fb335482-9577-41af-a0ef-3988b7654c9b
Delaney, Matthew
46e88672-435e-4f50-8df2-2aed6f3edbcd
Reynolds, Jamie
96faa744-02ee-458c-8e48-953ea9e54afe
Ebert, Martin
1a8f1756-d724-4b44-8504-c01f8dc7aa50
Zeimpekis, Ioannis
a2c354ec-3891-497c-adac-89b3a5d96af0
Thomson, David J.
17c1626c-2422-42c6-98e0-586ae220bcda
Reed, Graham T.
ca08dd60-c072-4d7d-b254-75714d570139
Lalanne, Philippe
2eaa3c11-ba6e-4de9-85d1-a3788f611964
Vynck, Kevin
fa5b6236-c2d0-47a3-90a9-5c849b9cb22f
Muskens, Otto L.
2284101a-f9ef-4d79-8951-a6cda5bfc7f9

Dinsdale, Nicholas J., Wiecha, Peter R., Delaney, Matthew, Reynolds, Jamie, Ebert, Martin, Zeimpekis, Ioannis, Thomson, David J., Reed, Graham T., Lalanne, Philippe, Vynck, Kevin and Muskens, Otto L. (2021) Deep learning enabled design of complex transmission matrices for universal optical components. ACS Photonics, 8 (1), 283-295. (doi:10.1021/acsphotonics.0c01481).

Record type: Article

Abstract

Recent breakthroughs in photonics-based quantum, neuromorphic, and analogue processing have pointed out the need for new schemes for fully programmable nanophotonic devices. Universal optical elements based on interferometer meshes are underpinning many of these new technologies, however, this is achieved at the cost of an overall footprint that is very large compared to the limited chip real estate, restricting the scalability of this approach. Here, we consider an ultracompact platform for low-loss programmable elements using the complex transmission matrix of a multiport multimode waveguide. We propose a deep learning inverse network approach to design arbitrary transmission matrices using patterns of weakly scattering perturbations. The demonstrated technique allows control over both the intensity and the phase in a multiport device at a four orders reduced device footprint compared to conventional technologies, thus, opening the door for large-scale integrated universal networks.

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MMI_DeepLearning_Shared (13) - Accepted Manuscript
Restricted to Repository staff only until 5 January 2022.
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More information

e-pub ahead of print date: 5 January 2021
Published date: 20 January 2021
Keywords: deep learning, inverse design, light scattering, silicon photonics

Identifiers

Local EPrints ID: 447569
URI: http://eprints.soton.ac.uk/id/eprint/447569
ISSN: 2330-4022
PURE UUID: fcf60faa-ef9d-467b-be5f-e568c2c23d59
ORCID for Nicholas J. Dinsdale: ORCID iD orcid.org/0000-0001-9870-5700
ORCID for Jamie Reynolds: ORCID iD orcid.org/0000-0002-0072-0134
ORCID for Otto L. Muskens: ORCID iD orcid.org/0000-0003-0693-5504

Catalogue record

Date deposited: 16 Mar 2021 17:32
Last modified: 18 Mar 2021 02:48

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