Machine Learning Enables Multi‐Degree‐of‐Freedom Reconfigurable Terahertz Holograms with Cascaded Diffractive Optical Elements
Source abstract
Abstract
Abstract Machine learning can empower the design of cascaded diffractive optical elements (DOEs) at terahertz frequencies enabling the realization of holograms with a tailored multi‐degree‐of‐freedom reconfigurable operation when altering either the number, spacing, rotational alignment, and/or order of the elements. This unprecedented control over the spatial terahertz light distribution can profoundly impact multiple terahertz applications such as signal multiplexing, imaging, and displays. This work demonstrates this multi‐degree‐of‐freedom control in structures fabricated through 3D printing employing low‐loss materials. The designs are validated through 3D finite‐difference time‐domain (FDTD) simulations and experimental measurements, showing that, in all cases, the desired diffraction patterns are generated.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Neural Networks and Reservoir Computing · Photonic and Optical Devices · Metamaterials and Metasurfaces Applications
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Electrical and Computer Engineering The University of Utah Salt Lake City UT 84112 USA
Funding
National Science Foundation
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