Abstract

High diffractive efficiency gratings, as a core component in optics, can engineer light transport and separation. This Letter predicts a grating surface with high diffractive efficiency within the visible light wave band with the aid of deep neural networks (DNNs). The predicted grating surface can have more than 99% diffractive efficiency for the −1th order within the bandwidth of ∼100 nm in the visible wave band, outperforming previously reported structures. Accordingly, the strategy of the DNN-aided design is an efficient and feasible method for optical devices. Moreover, changing the period of the predicted grating surfaces can shift the workable wave band, not only exhibiting the tunability but also bringing about the predicted gratings with more than 90% diffractive efficiency within the whole visible light wave band.

© 2022 Optica Publishing Group

Orthogonality of diffractive deep neural network

Shuiqin Zheng, Shixiang Xu, and Dianyuan Fan
Opt. Lett. 47(7) 1798-1801 (2022)

Prediction of metasurface spectral response based on a deep neural network

Ying Chen, Zhixin Ding, JianKun Wang, Jian Zhou, and Min Zhang
Opt. Lett. 47(19) 5092-5095 (2022)

Optical random micro-phase-shift DropConnect in a diffractive deep neural network

Yong-Liang Xiao, Sikun Li, Guohai Situ, and Jianxin Zhong
Opt. Lett. 47(7) 1746-1749 (2022)

Long short-term memory neural network for directly inverse design of nanofin metasurface

Wenqiang Deng, Zhengji Xu, Jinhao Wang, and Jinwen Lv
Opt. Lett. 47(13) 3239-3242 (2022)

Diffraction efficiency optimization for multilayered parametric holographic gratings

José Pinto, Rubén Aylwin, Gerardo Silva-Oelker, and Carlos Jerez-Hanckes
Opt. Lett. 46(16) 3929-3932 (2021)