Physics-Guided Neural-Network-Based Inverse Design of a Photonic – Plasmonic Nanodevice for Superfocusing
Physics-Guided Neural-Network-Based Inverse Design of a Photonic – Plasmonic Nanodevice for Superfocusing
复制标题
用于超聚焦的光子-等离子体纳米器件的物理引导基于神经网络的逆向设计
DOI：
10.1021/acsami.2c05083
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发表时间：
2022
影响因子：
9.5
通讯作者：
Liu, Ming
中科院分区：
文献类型：
--
作者：
Liang, Boqun;Xu, Da;Yu, Ning;Xu, Yaodong;Ma, Xuezhi;Liu, Qiushi;Asif, M. Salman;Yan, Ruoxue;Liu, Ming
使用超聚焦混合光子-等离子体装置控制纳米级光-物质相互作用引起了人们对解决现有挑战的巨大研究兴趣，包括转换效率、工作带宽和制造复杂性。随着对高效光子-等离子体输入-输出接口以提高等离子体器件性能的需求不断增长，需要具有多个优化参数的复杂设计，而这会带来难以承受的计算成本。与数值模拟相比，机器学习方法可以显着降低计算成本，但输入输出维度不匹配仍然是一个具有挑战性的问题。在这里，我们介绍了一种物理引导的两阶段机器学习网络，该网络使用改进的光波导耦合模式理论来指导学习模块，并将预测引擎的准确率提高到98.5％。通过逆向设计预测了具有对称破缺选择性的接近统一的耦合效率。通过使用预测的轮廓制造光子-等离子体耦合器，我们证明可以在径向偏振表面等离子体模式上实现 83% 的激发效率，这为超分辨率光学成像铺平了道路。
Controlling the nanoscale light–matter interaction using superfocusing hybrid photonic–plasmonic devices has attracted significant research interest in tackling existing challenges, including converting efficiencies, working bandwidths, and manufacturing complexities. With the growth in demand for efficient photonic–plasmonic input–output interfaces to improve plasmonic device performances, sophisticated designs with multiple optimization parameters are required, which comes with an unaffordable computation cost. Machine learning methods can significantly reduce the cost of computations compared to numerical simulations, but the input–output dimension mismatch remains a challenging problem. Here, we introduce a physics-guided two-stage machine learning network that uses the improved coupled-mode theory for optical waveguides to guide the learning module and improve the accuracy of predictive engines to 98.5%. A near-unity coupling efficiency with symmetry-breaking selectivity is predicted by the inverse design. By fabricating photonic–plasmonic couplers using the predicted profiles, we demonstrate that the excitation efficiency of 83% on the radially polarized surface plasmon mode can be achieved, which paves the way for super-resolution optical imaging.