Wang Qizhou, Makarenko Maksim, Burguete Lopez Arturo, Getman Fedor, Fratalocchi Andrea
PRIMALIGHT, Faculty of Electrical Engineering, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia.
Nanophotonics. 2021 Dec 22;11(11):2483-2505. doi: 10.1515/nanoph-2021-0660. eCollection 2022 Jun.
Nanophotonics inverse design is a rapidly expanding research field whose goal is to focus users on defining complex, high-level optical functionalities while leveraging machines to search for the required material and geometry configurations in sub-wavelength structures. The journey of inverse design begins with traditional optimization tools such as topology optimization and heuristics methods, including simulated annealing, swarm optimization, and genetic algorithms. Recently, the blossoming of deep learning in various areas of data-driven science and engineering has begun to permeate nanophotonics inverse design intensely. This review discusses state-of-the-art optimizations methods, deep learning, and more recent hybrid techniques, analyzing the advantages, challenges, and perspectives of inverse design both as a science and an engineering.
纳米光子学逆向设计是一个迅速发展的研究领域，其目标是让用户专注于定义复杂的高级光学功能，同时利用机器在亚波长结构中寻找所需的材料和几何结构配置。逆向设计的历程始于传统的优化工具，如拓扑优化和启发式方法，包括模拟退火、群体优化和遗传算法。最近，深度学习在数据驱动的科学和工程的各个领域蓬勃发展，已开始强烈渗透到纳米光子学逆向设计中。本文综述讨论了当前的优化方法、深度学习以及最新的混合技术，分析了逆向设计作为一门科学和一项工程的优势、挑战及前景。
