Physics-model-based neural networks for inverse design of binary phase planar diffractive lenses

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 random searching process. Here, we report an efficient inverse design method, based on physics-model-based neural networks (PMNN), for engineering the tightly focusing behavior of binary phase planar diffractive lenses (BPPDLs). We adopt the proposed PMNN to design BPPDLs with designable functionalities, including realizing single focal spot, multiple foci, and optical needle with size approaching the diffraction limit. We show that the time for designing single device is dramatically reduced to only several minutes. This study provides an efficient inverse design method for designing photonic device with customized functionalities, overcoming the challenges in inverse design based on traditional data-driven deep learning.