Opt Lett. 2021 Aug 15;46(16):3881-3884. doi: 10.1364/OL.427221.
For the design of achromatic metalenses, one key challenge is to accurately realize the wavelength dependent phase profile. Because of the demand of tremendous simulations, traditional methods are laborious and time consuming. Here, a novel deep neural network (DNN) is proposed and applied to the achromatic metalens design, which turns complex design processes into regression tasks through fitting the target phase curves. During training, - projection pairs are put forward to solve the phase jump problem, and some additional phase curves are manually generated to optimize the DNN performance. To demonstrate the validity of our DNN, two achromatic metalenses in the near-infrared region are designed and simulated. Their average focal length shifts are 2.6% and 1.7%, while their average relative focusing efficiencies reach 59.18% and 77.88%.
对于消色差金属透镜的设计，一个关键挑战是准确实现波长相关的相位分布。由于需要大量的模拟，传统方法既费力又耗时。在这里，提出了一种新的深度神经网络（DNN），并将其应用于消色差金属透镜的设计中，通过拟合目标相位曲线，将复杂的设计过程转化为回归任务。在训练过程中，提出了-投影对来解决相位跳跃问题，并手动生成一些附加的相位曲线来优化 DNN 的性能。为了验证我们的 DNN 的有效性，设计并模拟了两个近红外区的消色差金属透镜。它们的平均焦距偏移分别为 2.6%和 1.7%，而平均相对聚焦效率分别达到 59.18%和 77.88%。
