Photonic Meta-Neurons

Artificial 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. Metasurfaces that can optically mimic biological neurons, which are referred to as meta-neurons, avoid the drawbacks of electronic systems. Meta-neurons enable flexible modulation of optical signals through hierarchical and high-dimensional manipulation of light fields. The hierarchical simulations of the biological neurons are discussed in this perspective. Meta-neurons are a kind of potential photonic application of human brain science.image