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

摘要

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 machine-learning 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.