Towards retrieving dispersion profiles using quantum-mimic optical coherence tomography and machine learning

Abstract

Artefacts in quantum-mimic optical coherence tomography are considered detrimental because they scramble the images even for the simplest objects. They are a side effect of autocorrelation, which is used in the quantum entanglement mimicking algorithm behind this method. Interestingly, the autocorrelation imprints certain characteristics onto an artefact - it makes its shape and characteristics depend on the amount of dispersion exhibited by the layer that artefact corresponds to. In our method, a neural network learns the unique relationship between the artefacts' shape and GVD, and consequently, it is able to provide a good qualitative representation of object's dispersion profile for never-seen-before data: computer-generated single dispersive layers and experimental pieces of glass. We show that the autocorrelation peaks - additional peaks in the A-scan appearing due to the interference of light reflected from the object - affect the GVD profiles. Through relevant calculations, simulations and experimental testing, the mechanism leading to the observed GVD changes is identified and explained. Finally, the network performance is tested in the presence of noise in the data and with the experimental data representing single layers of quartz, sapphire and BK7.