基于深度学习的微环谐振腔法诺效应调控  

Regulation Method of Fano Resonance Effect Based on Deep Learning in Micro-ring Resonators

作　　者：孙鑫 李文秀 蒋硕 杨宗麒 黄馨瑶 张浩[1] 黄安平[3] 肖志松[3,4] SUN Xin;LI Wenxiu;JIANG Shuo;YANG Zongqi;HUANG Xinyao;ZHANG Hao;HUANG Anping;XIAO Zhisong(Institute of Advanced Science and Technology,Beihang University,Beijing 100191,China;School of Electronic and Information Engineering,Beihang University,Beijing 100191,China;School of Physics,Beihang University,Beijing 100191,China;School of Instrument Science and Opto-electronics Engineering,Beijing Information Science and Technology University,Beijing 100192,China)

机构地区：[1]北京航空航天大学前沿科学技术创新研究院,北京100191 [2]北京航空航天大学电子信息工程学院,北京100191 [3]北京航空航天大学物理学院,北京100191 [4]北京信息科技大学仪器科学与光电工程学院,北京100192

出　　处：《光子学报》2024年第5期158-168,共11页Acta Photonica Sinica

基　　金：国家自然科学基金(No.6197032364)。

摘　　要：在微腔耦合波导上设计空气孔阵列,结合深度学习调控微腔的法诺线型,实现对微腔传感性能的优化。建立了正向预测网络模型,在24 ms内实现从空气孔阵列结构到微腔透射谱线的预测。结合卷积神经网络与逆向设计实现了对微腔透射谱线的定向调控,预测了不同指标下空气孔阵列的趋势,验证了空气孔阵列结构与微腔透射特性的关系。To precisely control the transmission spectra of micro-ring resonators,a design incorporating arrays of etched air holes was introduced into the micro-ring resonator and coupled waveguide system.Modeling was completed using deep learning algorithms and inverse design techniques to predict the forward transmission spectra and optimize the performance of Fano resonance spectrum through inverse design.Based on the inverse design results,the slope of the transmission spectra was nearly doubled after 12 iterations of enhancement.For the preparation of the dataset,a system consisting of a single micro-ring resonator coupled with a straight waveguide was investigated.Six rows by six columns of air holes,each with a diameter of 100 nm,were introduced at both ends.Light enters the micro-ring resonator from the left design area and passes through to the right design area.The etched air hole arrays in these areas affect the propagation of light.Based on this micro-ring waveguide system,5000 samples were obtained using Finite-Difference Time-Domain(FDTD)simulations.These samples included the distribution of air hole arrays at both ends of the coupled waveguide and the corresponding micro-ring transmission spectra data.Utilizing the deep learning multi-layer perceptron algorithm,modeling was performed with the structure of the air hole arrays as input and the transmission spectra as output.This successfully enabled the prediction of micro-ring transmission output spectra within 24 ms for different air hole etchings.The cosine similarity values between the predicted spectra and traditional simulation spectra were close to 1.0.Next,an asymmetry index was defined and combined with the number of resonance peaks to distinguish between Fano and non-Fano line shapes in the dataset.To establish an inverse design model,the slope was defined as a performance indicator for the Fano resonance line shape in micro-ring resonators,representing the degree of tilt of the Fano line shape.This indicator was then used to control the Fano line sh

关 键 词：集成光学 微环谐振腔 法诺效应 逆向设计 深度学习 

分 类 号：TN256[电子电信—物理电子学]