Abstract

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, $x - y$ 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%.

© 2021 Optical Society of America

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