Design of Dual-Wavelength Bifocal Metalens Based on Generative Adversarial Network Model

Gangcheng LIU; Junkai WANG; Sen LIN; Binhe WU; Chunrui WANG; Jian ZHOU; Hao SUN

doi:10.19884/j.1672-5220.202404001

Journal of Donghua University(English Edition) ›› 2025, Vol. 42 ›› Issue (2) :168 -176. DOI: 10.19884/j.1672-5220.202404001

Information Technology and Artificial Intelligence

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Design of Dual-Wavelength Bifocal Metalens Based on Generative Adversarial Network Model

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Abstract

Multifocal metalenses are of great concern in optical communications, optical imaging and micro-optics systems, but their design is extremely challenging.In recent years, deep learning methods have provided novel solutions to the design of optical planar devices.Here, an approach is proposed to explore the use of generative adversarial networks(GANs) to realize the design of metalenses with different focusing positions at dual wavelengths.This approach includes a forward network and an inverse network, where the former predicts the optical response of meta-atoms and the latter generates structures that meet specific requirements.Compared to the traditional search method, the inverse network demonstrates higher precision and efficiency in designing a dual-wavelength bifocal metalens.The results will provide insights and methodologies for the design of tunable wavelength metalenses, while also highlighting the potential of deep learning in optical device design.

Keywords

generative adversarial network(GAN) / metalens / forward network / inverse design

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Gangcheng LIU, Junkai WANG, Sen LIN, Binhe WU, Chunrui WANG, Jian ZHOU, Hao SUN. Design of Dual-Wavelength Bifocal Metalens Based on Generative Adversarial Network Model. Journal of Donghua University(English Edition), 2025, 42(2): 168-176 DOI:10.19884/j.1672-5220.202404001

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