A machine learning-based analysis of a reconfigurable 1x2 logic gate operated through an externally-induced metamaterial

A. Fantoni; P. Di Giamberardino

doi:10.1117/12.3021692

18 June 2024 A machine learning-based analysis of a reconfigurable 1x2 logic gate operated through an externally-induced metamaterial

A. Fantoni, P. Di Giamberardino

Author Affiliations +

Proceedings Volume 13017, Machine Learning in Photonics; 1301713 (2024) https://doi.org/10.1117/12.3021692
Event: SPIE Photonics Europe, 2024, Strasbourg, France

Conference Poster

Abstract

A simulation approach, based on the beam propagation algorithm, has been used to produce a large dataset of simulations for a MMI structure, interfaced to a MOS controlled metasurface. A machine learning approach has been used to classify the MMI configuration in terms of binary digital output for a 1x2 logical gate. This proof of concept paves the way to a more complex device class, supporting the recent advances in programmable photonic integrated circuits.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

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A. Fantoni and P. Di Giamberardino "A machine learning-based analysis of a reconfigurable 1x2 logic gate operated through an externally-induced metamaterial", Proc. SPIE 13017, Machine Learning in Photonics, 1301713 (18 June 2024); https://doi.org/10.1117/12.3021692

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