Unidirectional Imaging using Deep Learning-Designed Materials

• DOI: 10.48550/arxiv.2212.02025
• Publication Date: 2022-01-01
• Authors: Jingxi Li; Tianyi Gan; Yifan Zhao; Bijie Bai; Che‐Yung Shen; Songyu Sun; Mona Jarrahi; Aydogan Özcan
• Keywords: Monochromatic color

A unidirectional imager would only permit image formation along one direction, from an input field-of-view (FOV) to output FOV B, and in the reverse path, be blocked. Here, we report first demonstration of imagers, presenting polarization-insensitive broadband imaging based on successive diffractive layers that are linear isotropic. These optimized using deep learning consist hundreds thousands phase features, which collectively modulate incoming fields project intensity onto FOV, while blocking direction. After their learning-based training, resulting fabricated form a imager. As reciprocal device, has asymmetric mode processing capabilities forward backward directions, where optical modes B selectively guided/scattered miss whereas for direction such modal losses minimized, yielding ideal system between FOVs. Although trained monochromatic illumination, maintains its functionality over large spectral band works under illumination. We experimentally validated this terahertz radiation, very well matching our numerical results. Using same design strategy, also created wavelength-selective imager, two operations, multiplexed through different illumination wavelengths. Diffractive structured materials will have numerous applications e.g., security, defense, telecommunications privacy protection.