All dielectric metasurface based diffractive neural networks for 1-bit adder

Abstract

Abstract Diffractive deep neural networks (D 2 NNs) have brought significant changes in many fields, motivating the development of diverse optical computing components. However, a crucial downside in the optical computing components is employing diffractive optical elements (DOEs) which were fabricated using commercial 3D printers. DOEs simultaneously suffer from the challenges posed by high-order diffraction and low spatial utilization since the size of individual neuron is comparable to the wavelength scale. Here, we present a design of D 2 NNs based on all-dielectric metasurfaces which substantially reduces the individual neuron size of net to scale significantly smaller than the wavelength. Metasurface-based optical computational elements can offer higher spatial neuron density while completely eliminate high-order diffraction. We numerically simulated an optical half-adder and experimentally verified it in the terahertz frequency. The optical half-adder employed a compact network with only two diffraction layers. Each layer has a size of 2 × 2 cm2 but integrated staggering 40,000 neurons. The metasurface-based D 2 NNs can further facilitate miniaturization and integration of all optical computing devices and will find applications in numerous fields such as terahertz 6G communication, photonics integrated circuits, and intelligent sensors.