Diffractive unidirectional imager designed by deep learning

We introduce a unidirectional imager that facilitates polarization-insensitive and broadband operation using isotropic, linear materials. This design comprises diffractive layers with hundreds of thousands of learnable phase features, trained using deep learning to enable power-efficient, high-fidelity imaging in the forward direction (A-to-B), while simultaneously inhibiting optical transmission and image formation in the reverse direction (B-to-A). We experimentally tested our designs using terahertz radiation, providing a good match with our simulations. Furthermore, we demonstrated a wavelength-selective unidirectional imager that performs unidirectional imaging along A-to-B at a predetermined wavelength, while at a second wavelength, the unidirectional operation switches from B-to-A.