Terahertz optical pattern recognition with rotation and scaling enhanced by a 3D-printed diffractive deep neural network

Abstract

Optical pattern recognition (OPR) has the potential to be a valuable tool in the field of terahertz (THz) imaging, with the advantage of being capable of image recognition with single-point detection, which reduces the overall system costs. However, this application is limited in the traditional OPR that rotation and scaling of the input image will bring about an offset of the recognition spot. Here we demonstrate a full-diffractive method to maintain the recognition spot at a fixed position, even when the input image is rotated or scaled, by using an all-optical diffractive deep neural network. The network is composed of two layers of diffractive optical elements (DOEs) without a 4f-system, and 3D-printed all-in-one. Experimental results show that our device can achieve a stable recognition of the input image regardless of its rotation (from 0° to 360°) or scaling (with a ratio from 1 to 1/1.9). This work is expected to provide enhanced functionality for compact THz systems in imaging and security applications.