光学精密工程2026，Vol.34Issue(9)：1453-1467,15.DOI:10.37188/OPE.20263409.1453
基于通道注意力增强网络的高效消色差超透镜逆向设计
Inverse design of efficient achromatic metalens based on channel attention enhanced network
摘要
Abstract
Rapid and accurate prediction of the phase spectral response of meta-units is essential for the in-verse design of metalenses.Conventional electromagnetic simulation methods are computationally inten-sive and inefficient,making them inadequate for the intelligent design of metasurface devices.To address this limitation,a convolutional neural network incorporating an efficient channel attention mechanism(EC-ANet)is proposed for forward prediction of the phase spectrum of pixelated meta-atom structures.Train-ing and validation on a simulated dataset demonstrate that the mean absolute error of phase prediction with-in the design bandwidth is below 0.06 rad.The trained ECANet is further integrated into a particle swarm optimization(PSO)algorithm to establish an efficient inverse design framework for achromatic metalens-es.Simulation results indicate that the designed metalens achieves a focal length variation of less than 2%across the operating bandwidth,demonstrating excellent achromatic focusing performance.By replacing conventional finite-difference time-domain(FDTD)simulations with rapid phase predictions from EC-ANet,the proposed framework improves computational efficiency by approximately two orders of magni-tude while maintaining design accuracy.This work presents a novel and efficient approach for the intelli-gent design of pixelated metasurface devices in the visible regime,with significant potential for applications in integrated optics,micro-imaging systems,and related fields.关键词
深度学习/消色差超透镜/逆向设计/粒子群优化算法Key words
deep learning/achromatic metalens/inverse design/particle swarm optimization algorithm分类
信息技术与安全科学引用本文复制引用
容仕翰,王启安,蔡云霄,张琬皎,徐卫明,张楠..基于通道注意力增强网络的高效消色差超透镜逆向设计[J].光学精密工程,2026,34(9):1453-1467,15.基金项目
国家自然科学基金资助项目(No.62575023) （No.62575023）
