Data-driven design approaches based on deep-learning have been introduced in nanophotonics to reduce time-consuming iterative simulations which have been a major challenge. Here, we report the first use of conditional deep convolutional generative adversarial networks to design nanophotonic antennae that are not constrained to a predefined shape. For given input reflection spectra, the network generates desirable designs in the form of images; this form allows suggestions of new structures that cannot be represented by structural parameters. Simulation results obtained from the generated designs agreed well with the input reflection spectrum. This method opens new avenues towards the development of nanophotonics by providing a fast and convenient approach to design complex nanophotonic structures that have desired optical properties.
翻译:以深层学习为基础的数据驱动设计方法已经引入纳米光学系统,以减少耗费时间的迭代模拟,这是一大挑战。在这里,我们报告首次使用有条件的深层进化基因对抗网络来设计不受预定形状限制的纳米光线。对于输入反射光谱,网络生成了以图像为形式的理想设计;这种形式允许对结构参数无法代表的新结构提出建议。从生成的设计中获得的模拟结果与输入反射频谱非常一致。这一方法为纳米光学的发展开辟了新的途径,为设计具有理想光学特性的复杂纳米光学结构提供了快速和方便的方法。