Inverse-Designed Multi-Level Diffractive Doublet for Wide Field-of-View Imaging

Abstract

Wide field-of-view (WFOV) imaging is extensively used in fields as diverse as macro photography, virtual reality, machine vision, and microscopy. Conventional WFOV cameras are bulky since they require multiple refractive lenses. Here, we design, fabricate, and characterize a 100° FOV camera with a total-track length of ∼2 mm, composed of an inverse-designed multi-level diffractive doublet with an input-aperture diameter of 0.204 mm, a back-focal length of 1 mm, and operating at λ = 850 nm (bandwidth ∼ 34 nm). Using careful experiments, we characterize the focusing and imaging performance as well as demonstrate de-vignetting based on experimental calibration. The spatial frequency at 10% modulation-transfer-function contrast was measured at 150 and 38 lp/mm for 0 and 50° incident angles, respectively. Our approach can be scaled to larger aperture sizes and smaller f/#s, potentially leading to thin, lightweight WFOV cameras for portable applications.

Compact eternal diffractive neural network chip for extreme environments

“…Over the years, artificial intelligence (AI) methods have been widely used in recognition 1 , autonomous driving 2 , scientific research 3 – 5 , human-computer interaction 6 , and robotics 7 . With the gradual development of AI capabilities, AI methods are expected to replace manual approaches in extreme environments, such as inclement weather, the deep sea, and space.…”