基于超材料的电磁神经网络研究进展

With the widespread application of artificial intelligence technology, the demand for computing power for intelligent computing has grown exponentially. At present, the rapid development of chips has approached the bottleneck of its manufacturing process, and power consumption is also increasing. Therefore, research on high-speed, energy-efficient intelligent computing hardware is an important direction. Computing architectures represented by photonic circuit neural networks and all-optical diffraction neural networks have received widespread attention due to their advantages such as fast calculation and low power consumption. This article reviews the representative work of optical neural networks, and introduces it through the two main lines of development of three-dimensional diffractive neural networks and optical neural network chips. At the same time, it focuses on the bottlenecks and challenges faced by optical diffractive neural networks and photonic neural network chips, such as network scale and Integration degree, etc., analyzes and compares their characteristics, performance and respective advantages and disadvantages. Secondly, taking into account the development needs of generalization, this article further discusses the programmable design of neuromorphic computing hardware, and introduces some representative work on programmable neural networks to each part. In addition to intelligent neural networks in the optical band, this article also discusses the development and application of microwave diffraction neural networks and demonstrates their programmability. Finally, the future direction and development trends of intelligent neuromorphic computing are introduced, as well as its potential applications in wireless communications, information processing and sensing. © 2024 Science Press. All rights reserved.