Designing nanophotonic structures using conditional deep convolutional generative adversarial networks

Nanophotonics

article

Sunae So¹  Junsuk Rho¹ 

[1] Department of Mechanical Engineering, Pohang University of Science and Technology (POSTECH);Department of Chemical Engineering, Pohang University of Science and Technology (POSTECH)

关键词: nanophotonics;    inverse design;    conditional deep convolutional generative adversarial network;    deep learning;   

DOI  :  10.1515/nanoph-2019-0117

学科分类：社会科学、人文和艺术（综合）

来源: De Gruyter

【 摘 要 】

Data-driven design approaches based on deep learning have been introduced in nanophotonics to reduce time-consuming iterative simulations, which have been a major challenge. Here, we report the first use of conditional deep convolutional generative adversarial networks to design nanophotonic antennae that are not constrained to predefined shapes. For given input reflection spectra, the network generates desirable designs in the form of images; this allows suggestions of new structures that cannot be represented by structural parameters. Simulation results obtained from the generated designs agree well with the input reflection spectrum. This method opens new avenues toward the development of nanophotonics by providing a fast and convenient approach to the design of complex nanophotonic structures that have desired optical properties.

【 授权许可】

CC BY