Deep learning for the design and characterization of high efficiency self-focusing grating

• Published in: Optics communications 2022-05, Vol.510, p.127951, Article 127951
• Main Authors: Pu, Tanchao, Cao, Fulin, Liu, Ziwei, Xie, Changqing
• Format: Article
• Language: English
• Subjects: Diffraction grating; Neural network; Optical structures design; Self-focusing
• ISSN: 0030-4018; 1873-0310
• DOI: 10.1016/j.optcom.2022.127951

We demonstrate that the deep learning algorithm can considerably simplify the design and characterization of high efficient self-focusing varied line-spaced gratings. Our neural network is implemented with a recovery rate of up to 94% for the transmission function parameters. With numerical simulations, and optical experiments, we show that the self-focusing varied line-spaced gratings designed in such a way are endowed with enhanced functionalities, such as the intensity of first-order diffraction peak being enhanced with around a factor of 30 compared with the incident intensity, and a high ratio (about 60) between the peak intensity of the first order and the intensity of the zero-order. Our results allow the rapid design and characterization of self-focusing varied line-spaced gratings as well as optimal microstructures for targeted far-field diffraction patterns, which are playing key roles in spectroscopy and monochromatization applications. •A novel deep learning based self-focusing varied line spaced grating design method is proposed.•The grating parameters are retrieved from desired pattern with 94% recovery rate.•The experimental peak intensity ratio between first order and zero order of the binary 1D grating can be up to 86.•The high performance self-focusing grating is very easy to fabricate.