All-optical diffractive neural networked terahertz hologram

Holography has garnered an explosion of interest in tremendous applications, owing to its capability of storing amplitude and phase of light and reconstructing the full-wave information of targets. Spatial light modulators, metalenses, metasurfaces, and other devices have been explored to achieve holographic images. However, the required phase distributions for conventional holograms are generally calculated using the Gerchberg-Saxton algorithm, and the iteration is time-consuming without Fourier transform or other acceleration techniques. Few studies on designing holograms using artificial intelligence methods have been conducted. In this Letter, a three-dimensional (3D)-printed hologram for terahertz (THz) imaging based on a diffractive neural network (DNN) is proposed. Target imaging letters "THZ" with uniform field amplitude are assigned to a predefined imaging surface. Quantified phase profiles are primarily obtained by training the DNN with the target image and input field pattern. The entire training process takes only 60 s. Consequently, the hologram, that is, a two-dimensional array of dielectric posts with variational heights that store phase information, is fabricated using a 3D printer. The full-wave simulation and experimental results demonstrate the capability of the proposed hologram to achieve high-quality imaging in the THz regime. The proposed lens and design strategy may open new possibilities in display, optical-data storage, and optical encryption.