Dynamic 3D holographic displays

We aim to develop novel computational graphic techniques that allow unprecedented realism using a dynamic holographic 3D display. The development of 3D displays with a high spatial and temporal resolution, for multiple points of view, is a very complex problem in state-of-art physics. There are different techniques for such porpuses, like the light-field displays or multiplayer LCDs. However, those techniques have several limitations to generate a continuous 3D display for different points of view. Instead, holography is without a doubt one of the preferent techniques to display continuous real 3D information for multiple viewpoints, and that is why it is worth investigating how to produce dynamic holographic videos.

Static 3D images can be generated with holographic techniques at such a high resolution that they seem practically real to the human eye. However, that much resolution has not yet been achieved with dynamic digital holographic displays. The current problem with dynamic holography is that the display's resolution (Space Bandwidth Product) is really limited. We propose to move on to a new paradigm: Knowing the nature of the images that will be displayed, a data-driven 3D display will reduce the possible light-fields to those which are physically plausible, for these specific images.

These novel 3D display technologies are linked to Augmented and Virtual Reality headsets, and there are potential applications in multiple fields, including medical surgery with AR, interactive 3D videoconferences, videogames, and cinema.

Wave propagation in scattering media

We explore how optical waves propagate using different approaches: The Finite Different Time Domain method (FDTD), the Finite Element Method (FEM), Fresnel/Fraunhofer propagation, the Angular Spectrum method (ASM), and machine learning techniques. 

We also analyze some applications in nonlinear optics (NL), nano-printed Optical Diffraction Elements (ODE), holography simulations, Computed Tomography, and Optical Diffraction Tomography (ODT).

Optical characterization of dielectric thin films

We perform optical characterization of dielectric thin films by analyzing the normal transmission spectra or ellipsometric measurements. We use optimization and machine learning techniques to combine these well-known traditional optical methods with some state-of-art algorithms from computer science. 

Optical characterization is relevant in manufacturing and quality engineering, where some materials are analyzed to determine if they have the desired characteristics.  We are focused on the analysis of Silicon, such as a:Si, a:SiH, and SiO2.  Some specific applications of those components can be found in solar cells, Li-ion batteries, modern LCDs, etc.