Review for artificial intelligence-based micro-nanophotonic devices
Zhichuan Xie, Huizhen Feng, Manna Gu, Xiaomei Zhang, Ying Tian, Le Wang, Xufeng Jing · Frontiers of Physics · 2025
Nanophotonic is an emerging frontier that explores the interaction between light and matter at the nanoscale. The design of ultra-compact and high-performance photonic devices remains a central challenge in this field. Conventional forward design approaches, which often rely on empirical knowledge, are computationally demanding and offer limited flexibility. The advent of deep learning has enabled inverse design strategies that start from desired optical responses. This review first outlines the background of traditional photonic device design and introduces machine learning techniques, particularly deep learning, for modeling nanostructures. It then examines the application of advanced optimization methods, including topology optimization and genetic algorithms, in the design of devices such as meta-lens, meta-gratings, and planar beam splitters. These examples highlight the integration of optoelectronics with artificial intelligence. The interplay between light manipulation and intelligent algorithms is accelerating advancements in optical design, imaging, communications, and data analysis, opening new avenues for interdisciplinary innovation.