Deep learning is now playing a major role in designing photonic devices, including nanostructured photonics. In this article, we investigate three models for designing nanophonic power splitters with multiple splitting ratios. The first model is a forward regression model, wherein the trained deep neural network (DNN) is used within the optimization loop. The second is an inverse regression model, in which the trained DNN constructs a structure with the desired target performance given as input. The third model is a generative network, which can randomly produce a series of optimized designs for a target performance. Focusing on the nanophotonic power splitters, we show how the devices can be designed by these three types of DNN models.

中文翻译：

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深度神经网络用于纳米光子器件的逆设计

深度学习现在在设计光子器件（包括纳米结构光子）中扮演着重要角色。在本文中，我们研究了三种设计具有多个分光比的纳音功率分配器的模型。第一模型是正向回归模型，其中在优化循环内使用训练的深度神经网络（DNN）。第二个是逆回归模型，其中训练有素的DNN构造一个具有所需目标性能作为输入的结构。第三种模型是生成网络，可以随机产生一系列针对目标性能的优化设计。着眼于纳米光子功率分配器，我们展示了如何通过这三种类型的DNN模型来设计设备。