Machine learning meets photonics

Francesco Da Ros; Metodi P Yankov; Darko Zibar

doi:10.1117/12.2676656

28 September 2023 Machine learning meets photonics

Francesco Da Ros, Metodi P Yankov, Darko Zibar

Proceedings Volume 12655, Emerging Topics in Artificial Intelligence (ETAI) 2023; 126550C (2023) https://doi.org/10.1117/12.2676656
Event: SPIE Nanoscience + Engineering, 2023, San Diego, California, United States

Abstract

Machine learning (ML) is becoming a ubiquitous and powerful tool helping to address challenges in countless fields. Applications of ML addressing optics challenges have been extensively studied in recent years opening up new research directions. In particular, here, we review some of our current efforts and provide examples of successful applications of ML to the characterization of photonic devices, design, and modeling of optical subsystems, and complete end-to-end optical system optimization. ML and statistical tools can yield additional insight from measurement data, e.g. by targeted filtering of noise sources. They have also been shown to assist complex or inaccurate physics-based models through black and grey-box modeling of photonics components or subsystems. Such ML-aided models have enabled easier optimization and design (including inverse design) of optical systems.

Conference Presentation

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Francesco Da Ros, Metodi P Yankov, and Darko Zibar "Machine learning meets photonics", Proc. SPIE 12655, Emerging Topics in Artificial Intelligence (ETAI) 2023, 126550C (28 September 2023); https://doi.org/10.1117/12.2676656

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