AI-based analysis of extremely low-resolved spectrogram fingerprints for the calculation of surface profiles in low-coherence interferometry

Low-coherence-interferometry (LCI) is a powerful and widely used measurement approach in the fields of biomedicine, surface analysis, and imaging. Many techniques, such as optical coherence tomography (OCT) or Dispersion-Encoded-LCI (DE-LCI) derive from LCI. This work focuses on the DE-LCI measurement approach for profilometry. An estimation of axial displacement in the measuring arm of extremely low resolved DE-LCI spectrograms was achieved by instrumentalizing an artificial intelligence (AI) based analysis technique. It was proven effective even for spectrograms that partially fall below the Nyquist criterion. The presented estimation strategy considers the very low-resolution distorted data to be some kind of "fingerprint" of the complete initial signal, which cannot be interpreted directly by classic deterministic models. It was shown, that this resolution limitation could be exceeded for certain boundary conditions with the introduced artificial neural network topology. The benefits of the proposed AI-Model are demonstrated in a series of reference measurements of the surface topography of an uncoated Si-reference object. The spectral resolution was varied throughout the process. The relation between the absolute axial resolution and full measurement range was used to evaluate the final measurement dynamic. Resistance to noise and mechanical displacement, especially the displacement of the reference and the typical detector noise is presented and discussed for the proposed estimation approach. The described novel method allows overcoming central instrumental limitations, namely the spectral resolution of the used instrument, while increasing measurement dynamics significantly. This is crucial for DE-LCI sensors applications as well as for in-line and high-speed DE-LCI metrology purposes.