Kakkava, EiriniBorhani, NavidRahmani, BabakTeğin, UğurMoser, ChristophePsaltis, Demetri2020-07-162020-07-162020-07-162020-05-3010.3390/app10113816https://infoscience.epfl.ch/handle/20.500.14299/170193Deep neural networks (DNNs) are employed to recover information after its propagation through a multimode fiber (MMF) in the presence of wavelength drift. The intensity distribution of the speckle patterns generated at the output of an MMF when an input wavefront propagates along its length is highly sensitive to wavelength changes. We use a tunable laser to implement a wavelength drift with a controlled bandwidth, aiming to estimate the DNN’s performance in different cases and identify the limitations. We find that when the DNNs are trained with a dataset which includes the noise induced by wavelength changes, successful classification of a speckle pattern can be performed even for a large wavelength bandwidth drift. A single training step is found to be sufficient for high classification accuracy, removing the need for time-consuming recalibration at each wavelength.Multimode fibersDeep learningImage classificationFiber-based optical computingWavelength drifttext::journal::journal article::research article