Deep-learning informed design of unitary operators in silicon photonics using programmable phase change materials.

Thomas Radford; Peter Wiecha; Otto Muskens; Alberto Politi

doi:10.1117/12.3021544

18 June 2024 Deep-learning informed design of unitary operators in silicon photonics using programmable phase change materials.

Thomas Radford, Peter Wiecha, Otto Muskens, Alberto Politi

Author Affiliations +

Proceedings Volume PC13017, Machine Learning in Photonics; PC1301709 (2024) https://doi.org/10.1117/12.3021544
Event: SPIE Photonics Europe, 2024, Strasbourg, France

Abstract

By integrating chalcogenide phase change materials to the fabrication of coupled silicon waveguide arrays we can manipulate the propagation of light through photonic devices, by the introducing nanoscale refractive index perturbations. Prediction of the required pixel pattern needed to produce a given unitary transmission matrix is a complex problem. In order to optimize for multiple input and outputs simultaneously we employ an artificial neural network, for both forward prediction and inverse design. This work hopes to pave the way towards all optical computation and the production of reconfigurable analogue quantum simulators.

Conference Presentation

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Thomas Radford, Peter Wiecha, Otto Muskens, and Alberto Politi "Deep-learning informed design of unitary operators in silicon photonics using programmable phase change materials.", Proc. SPIE PC13017, Machine Learning in Photonics, PC1301709 (18 June 2024); https://doi.org/10.1117/12.3021544

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