Deep Learning Enabled Design of Complex Transmission Matrices for Universal Optical Components-Reference-Cited by-同舟云学术

Deep Learning Enabled Design of Complex Transmission Matrices for Universal Optical Components

Published:2021-01-05 Issue:1 Volume:8 Page:283-295
ISSN:2330-4022
Container-title:ACS Photonics
language:en
Short-container-title:ACS Photonics

Author:

Dinsdale Nicholas J.¹²,Wiecha Peter R.²³^ORCID,Delaney Matthew¹²,Reynolds Jamie¹,Ebert Martin¹,Zeimpekis Ioannis¹,Thomson David J.¹,Reed Graham T.¹,Lalanne Philippe⁴^ORCID,Vynck Kevin⁴,Muskens Otto L.²^ORCID

Affiliation:

1. Optoelectronics Research Centre, University of Southampton, SO17 1BJ Southampton, United Kingdom

2. Physics and Astronomy, Faculty of Engineering and Physical Sciences, University of Southampton, SO17 1BJ Southampton, United Kingdom

3. LAAS, Université de Toulouse, CNRS, 31031 Toulouse, France

4. LP2N, CNRS, Institut d’Optique Graduate School, Université de Bordeaux, F-33400 Talence, France

Funder

Engineering and Physical Sciences Research Council

Deutsche Forschungsgemeinschaft

CALMIP Toulouse

Publisher

American Chemical Society (ACS)

Subject

Electrical and Electronic Engineering,Atomic and Molecular Physics, and Optics,Biotechnology,Electronic, Optical and Magnetic Materials

Link

https://pubs.acs.org/doi/pdf/10.1021/acsphotonics.0c01481

Reference65 articles.

1. Programmable Photonics: An Opportunity for an Accessible Large-Volume PIC Ecosystem

2. Post-fabrication phase trimming of Mach–Zehnder interferometers by laser annealing of germanium implanted waveguides

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4. Integrated optical switch matrices for packet data networks

5. Deep learning with coherent nanophotonic circuits

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