Nanophotonic devices, such as metasurfaces and silicon photonic components, have been progressively demonstrated to be efficient and versatile alternatives to their bulky counterparts, enabling compact and light-weight systems for the application of imaging, sensing, communication and computing. The tremendous advances in machine learning provide new design methods, metrology and functionalities for nanophotonic devices and systems. Specifically, machine learning has fundamentally changed automatic design, measurement and result processing of highly application-specific nanophotonic systems without the need of extensive expert experience. This trend can be well described by the popular concept of “software-defined” infrastructure in information technology, which can decouple specific hardware from end users by virtualizing physical components using software interfaces, making the entire system faster, more flexible and more scalable. In this review, we introduce the concept of software-defined nanophotonics and summarize the interdisciplinary research that bridges nanophotonics and intelligence algorithms, especially machine learning algorithms, in the device design, measurement and system setup. The review is organized in an application-oriented manner, showing how the software-defined scheme is utilized in solving both forward and inverse problems for various nanophotonic devices and systems.

纳米光子器件，如超表面和硅光子元件，已逐渐被证明是其笨重同类产品的高效和多功能替代品，为成像、传感、通信和计算应用提供了紧凑和轻便的系统。机器学习的巨大进步为纳米光子器件和系统提供了新的设计方法、计量学和功能。具体来说，机器学习从根本上改变了高度专用纳米光子系统的自动设计、测量和结果处理，而无需丰富的专家经验。这种趋势可以用信息技术中流行的“软件定义”基础架构概念来很好地描述，它可以通过使用软件接口虚拟化物理组件来将特定硬件与最终用户分离，从而使整个系统更快、更灵活且更具可扩展性。在这篇综述中，我们介绍了软件定义的纳米光子学的概念，并总结了在设备设计、测量和系统设置中将纳米光子学和智能算法，特别是机器学习算法联系起来的跨学科研究。该评论以面向应用的方式组织，展示了如何利用软件定义的方案来解决各种纳米光子器件和系统的正向和逆向问题。我们介绍了软件定义的纳米光子学的概念，并总结了在设备设计、测量和系统设置中连接纳米光子学和智能算法，尤其是机器学习算法的跨学科研究。该评论以面向应用的方式组织，展示了如何利用软件定义的方案来解决各种纳米光子器件和系统的正向和逆向问题。我们介绍了软件定义的纳米光子学的概念，并总结了在设备设计、测量和系统设置中连接纳米光子学和智能算法，尤其是机器学习算法的跨学科研究。该评论以面向应用的方式组织，展示了如何利用软件定义的方案来解决各种纳米光子器件和系统的正向和逆向问题。