Modular Diffractive Neural Networks Using Cascaded Metasurfaces

Abstract

An all-optical diffractive neural network (DNN), as the fusion of optics and artificial intelligence, utilizes light-based computational architecture, providing potential beyond Moore's law limitations due to their low energy consumption and high parallel processing speed. However, existing DNN frameworks face limitations in realizing spatially assembled architecture (i.e., by combining two or more independent physical layers together) to create a Lego-like reconfigurable DNN that can generate additional functionalities. In this work, the modular programming concept is introduced to develop the modular diffractive neural networks (MDNNs) using the cascaded metasurfaces where each metasurface enables respective functionalities while the cascaded metasurfaces possess additional functionalities. The MDNNs showcase great advantages in flexibility and reconfigurability for multitask functionalities. When working independently, two metasurface-based modules can function as classifiers of handwritten letters and fashion products, respectively. When the two modules are assembled with different spatial orders, the additional functions of the handwritten digits classifier and imager can be obtained. Moreover, the MDNNs can be designed to mimic an architecture for high-capacity encrypted communication. This flexible and multiplexed MDNNs framework shows great potential and paves the new way for all-optical computation with multifunctional integration, massively parallel processing, and all-optical information security.