Pluggable multitask diffractive neural networks based on cascaded metasurfaces

Abstract

Optical neural networks have significant advantages in terms of power consumption, parallelism, and high computing speed, which has intrigued extensive attention in both academic and engineering communities. It has been considered as one of the powerful tools in promoting the fields of imaging processing and object recognition. However, the existing optical system architecture cannot be reconstructed to the realization of multi-functional artificial intelligence systems simultaneously. To push the development of this issue, we propose the pluggable diffractive neural networks (P-DNN), a general paradigm resorting to the cascaded metasurfaces, which can be applied to recognize various tasks by switching internal plug-ins. As the proof-of-principle, the recognition functions of six types of handwritten digits and six types of fashions are numerical simulated and experimental demonstrated at near-infrared regimes. Encouragingly, the proposed paradigm not only improves the flexibility of the optical neural networks but paves the new route for achieving high-speed, low-power and versatile artificial intelligence systems.

Keywords

• optical neural networks
• diffractive deep neural networks
• cascaded metasurfaces