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