Artificial intelligence has prevailed in all trades and professions due to the assistance of big data resources, advanced algorithms, and high-performance electronic hardware. However, conventional computing hardware is inefficient at implementing complex tasks, in large part because the memory and processor in its computing architecture are separated, performing insufficiently in computing speed and energy consumption. In recent years, optical neural networks (ONNs) have made a range of research progress in optical computing due to advantages such as sub-nanosecond latency, low heat dissipation, and high parallelism. ONNs are in prospect to provide support regarding computing speed and energy consumption for the further development of artificial intelligence with a novel computing paradigm. Herein, we first introduce the design method and principle of ONNs based on various optical elements. Then, we successively review the non-integrated ONNs consisting of volume optical components and the integrated ONNs composed of on-chip components. Finally, we summarize and discuss the computational density, nonlinearity, scalability, and practical applications of ONNs, and comment on the challenges and perspectives of the ONNs in the future development trends.

得益于大数据资源、先进算法、高性能电子硬件的助力，人工智能已遍及各行各业。然而，传统的计算硬件在执行复杂任务时效率低下，很大程度上是因为其计算架构中的内存和处理器是分离的，在计算速度和能耗方面表现不足。近年来，光神经网络（ONN）凭借亚纳秒级延迟、低散热、高并行性等优势，在光计算领域取得了一系列研究进展。 ONN有望通过新颖的计算范式为人工智能的进一步发展提供计算速度和能耗方面的支持。在这里，我们首先介绍基于各种光学元件的ONN的设计方法和原理。然后，我们依次回顾了由体积光学元件组成的非集成ONN和由片上元件组成的集成ONN。最后，我们对ONN的计算密度、非线性、可扩展性和实际应用进行了总结和讨论，并对ONN在未来发展趋势中面临的挑战和前景进行了评论。