Abstract Machine learning offers the potential to revolutionize the inverse design of complex nanophotonic components. Here, we propose a novel variant of this formalism specifically suited for the design of resonant nanophotonic components. Typically, the first step of an inverse design process based on machine learning is training a neural network to approximate the non-linear mapping from a set of input parameters to a given optical system’s features. The second step starts from the desired features, e.g. a transmission spectrum, and propagates back through the trained network to find the optimal input parameters. For resonant systems, this second step corresponds to a gradient descent in a highly oscillatory loss landscape. As a result, the algorithm often converges into a local minimum. We significantly improve this method’s efficiency by adding the Fourier transform of the desired spectrum to the optimization procedure. We demonstrate our method by retrieving the optimal design parameters for desired transmission and reflection spectra of Fabry–Pérot resonators and Bragg reflectors, two canonical optical components whose functionality is based on wave interference. Our results can be extended to the optimization of more complex nanophotonic components interacting with structured incident fields.

中文翻译：

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用于具有振荡损耗景观的谐振纳米光子元件逆向设计的人工神经网络

摘要 机器学习提供了彻底改变复杂纳米光子组件逆向设计的潜力。在这里，我们提出了这种形式主义的一种新变体，特别适用于谐振纳米光子组件的设计。通常，基于机器学习的逆向设计过程的第一步是训练神经网络以近似非线性映射从一组输入参数到给定光学系统的特征。第二步从所需特征（例如传输频谱）开始，并通过训练后的网络向回传播以找到最佳输入参数。对于谐振系统，这第二步对应于高度振荡损失景观中的梯度下降。结果，该算法经常收敛到局部最小值。我们通过将所需频谱的傅立叶变换添加到优化过程中，显着提高了该方法的效率。我们通过检索法布里-珀罗谐振器和布拉格反射器的所需透射和反射光谱的最佳设计参数来演示我们的方法，这两种标准光学组件的功能基于波干涉。我们的结果可以扩展到与结构化入射场相互作用的更复杂的纳米光子组件的优化。