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Unidirectional imaging using deep learning-designed materials
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DOI：10.1126/sciadv.adg1505.png)
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Abstract
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A unidirectional imager would only permit image formation along one direction, from an input field-of-view (FOV) A to an output FOV B, and in the reverse path, B -> A, the image formation would be blocked. We report the first demonstration of unidirectional imagers, presenting polarization-insensitive and broadband uni-directional imaging based on successive diffractive layers that are linear and isotropic. After their deep learning- based training, the resulting diffractive layers are fabricated to form a unidirectional imager. Although trained using monochromatic illumination, the diffractive unidirectional imager maintains its functionality over a large spectral band and works under broadband illumination. We experimentally validated this unidirectional imager using terahertz radiation, well matching our numerical results. We also created a wavelength-selective unidirec-tional imager, where two unidirectional imaging operations, in reverse directions, are multiplexed through dif-ferent illumination wavelengths. Diffractive unidirectional imaging using structured materials will have numerous applications in, e.g., security, defense, telecommunications, and privacy protection.
Keywords:
OPTICAL ISOLATION
LENS DESIGN
PROPAGATION
DIFFRACTION
MICROSCOPY
PLANES
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