Add to BibSonomy
Abstract
The inverse design approach has enabled the customized design of photonic devices with engineered functionalities through adopting various optimization algorithms. However, conventional optimization algorithms for inverse design encounter difficulties in multi-constrained problems due to the substantial time consumed in the random searching process. Here, we report an efficient inverse design method, based on physics-model-based neural networks (PMNNs) and Rayleigh–Sommerfeld diffraction theory, for engineering the focusing behavior of binary phase planar diffractive lenses (BPPDLs). We adopt the proposed PMNN to design BPPDLs with designable functionalities, including realizing a single focal spot, multiple foci, and an optical needle with size approaching the diffraction limit. We show that the time for designing single device is dramatically reduced to several minutes. This study provides an efficient inverse method for designing photonic devices with customized functionalities, overcoming the challenges based on traditional data-driven deep learning.
© 2023 Optica Publishing Group
Full Article | PDF ArticleMore Like This
Yuyao Chen, Yilin Zhu, Wesley A. Britton, and Luca Dal Negro
Opt. Lett. 47(11) 2842-2845 (2022)
Yuan Qu, Hengyu Lian, Chunxu Ding, Huazhen Liu, Linxian Liu, and Jiamiao Yang
Opt. Lett. 48(19) 5025-5028 (2023)
Ronghui Lin, Yanfen Zhai, Chenxin Xiong, and Xiaohang Li
Opt. Lett. 45(6) 1362-1365 (2020)