Machine Learning Enables Multi‐Degree‐of‐Freedom Reconfigurable Terahertz Holograms with Cascaded Diffractive Optical Elements. Issue 7 (9th February 2023)

• Record Type: Journal Article
• Authors: Jia, Wei; Lin, Dajun; Sensale‐Rodriguez, Berardi
• Is Part Of: Advanced optical materials. Volume 11:Issue 7(2023)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2023-02-09
• Subjects: diffractive optical elements -- holograms -- machine learning -- terahertz; Optical materials -- Periodicals; Photonics -- Periodicals; 620.11295
• DOI: 10.1002/adom.202202538
• Languages: English
• ISSNs: 2195-1071

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. Abstract : Multi‐degree‐of‐freedom reconfigurable terahertz holograms consisting of cascaded diffractive optical elements are designed through machine learning and fabricated by 3D printing. These devices can provide on‐demand control over the spatial terahertz light distribution by altering either the position, rotational alignment, order, or number of layers in the network, providing flexibility that can profoundly impact multiple terahertz applications.