Photonic Meta‐Neurons

Abstract

AbstractArtificial intelligence (AI) has witnessed a growing integration into numerous domains of production and daily life. Such powerful AI has emerged from electronic simulations of human neurons. In the post‐Moore era, Von‐Neumann architecture and high energy consumption have posed challenges for the advancement of electronic AI. In light of these limitations, a new concept of photonic meta‐neurons is proposed, which holds the potential to address these issues by optically emulating biological neurons through the utilization of metasurfaces. Meta‐neurons could enable flexible modulation of photonic signals through hierarchical and high‐dimensional manipulation of light fields. The flat and thin design of the meta‐neuron reduces the spatial constraints. In comparison to electronic AI, meta‐neurons offer a range of desirable attributes, including the potential for achieving light‐speed processing, parallel computing, clean energy utilization, high energy efficiency, and an in‐memory computing architecture. The meta‐neuron concept can serve as a promising starting point for photonic AI. With its advantages of high‐speed computing and environmental friendliness, meta‐neurons will have broad applications in various fields, particularly for latency‐critical tasks.