09- Exploiting Tailored Disorder in Dielectric Nanosurfaces to Maximize their Information Capacity
Source: https://www.cs.tum.de/spp1839/projects/2nd-period-2018-2021/09.html Parent: https://www.cs.tum.de/spp1839/projects.html
Dielectric nanosurfaces imprint information onto an incident field by spatially varying the field's phase and/or amplitude deterministically using strongly scattering objects. The highest efficiency is achieved if these scattering objects are made from low-loss high-permittivity materials. If operated at their duality point, perfect transmission is achieved. This unlocks a plethora of application in lighting and imaging devices, ultra-thin display technologies, for wavefront manipulation, and beam shaping.
However, the ability to control light disruptively comes at the expense of a pronounced long-range interaction in the surface plane. This limits the information density if these scatterers are arranged periodically on the surface.
In our project, we aim to improve the information capacity encoded into a nanosurface by introducing tailored disorder in the arrangement and the parameters of the scatterers on the surface. We aim to maximize the number of channels that can be encoded with the same area of the nanosurface and the information density encoded in each channel.
Our project comprises three research strands:
- We explore wavefront-shaping nanosurfaces where the information density is enhanced by explicitly exploiting disorder. The disorder is spatially tailored to adjust the local amplitude and phase of the transmitted light. This full control within a single material layer will be used to implement high-definition holograms. We aim to exploit the spectral/angular dispersive response to implement holograms at different wavelengths/illumination fields.
- We will use disorder to tailor the diffusive scattering in selected demonstrators. We will study devices that redirect the incident light in a continuous fashion and probe to which extent a nanosurface can mimic the response from an ordinary 3D random medium.
Contributors
Prof. Isabelle Staude
Prof. Carsten Rockstuhl
Prof. Thomas Pertsch
Aso Rahimzadegan
Dennis Arslan
Najmeh Abbasirad
References
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