Machine-learning-aided photonic hardware implementation incorporating natural optical phenomena

Tsuyoshi Konishi; Ryohei Kamikawa; Yu Yamasaki

doi:10.1117/12.2578029

5 March 2021 Machine-learning-aided photonic hardware implementation incorporating natural optical phenomena

Tsuyoshi Konishi, Ryohei Kamikawa , Yu Yamasaki

Proceedings Volume 11703, AI and Optical Data Sciences II; 1170307 (2021) https://doi.org/10.1117/12.2578029
Event: SPIE OPTO, 2021, Online Only

Abstract

Implementation of some signal processing algorithms on hardware has generally an advantage of efficiently implementation of complex processing. However, it still has some difficulties of developing natural optical phenomena because of various trade-off relation. Since these difficulties do not always allow a photonic hardware to emulate such an intermediary processing, further little assistances are necessary to complete the gap bridge and various machinelearning would play a significant role there. We discuss machine-learning-aided photonic hardware implementation incorporating natural optical phenomena with an example of a spectroscopic inspection technique for low cost, high speed, large data, and high spectral resolution.

Conference Presentation

Citation Download Citation

Tsuyoshi Konishi, Ryohei Kamikawa , and Yu Yamasaki "Machine-learning-aided photonic hardware implementation incorporating natural optical phenomena", Proc. SPIE 11703, AI and Optical Data Sciences II, 1170307 (5 March 2021); https://doi.org/10.1117/12.2578029

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