摘要
波导的传输损耗是评价集成光学平台性能的一个关键指标。常用的测量传输损耗的cut-back测试方法需引入弯曲波导测试结构。为了去除弯曲损耗的影响,通常会将弯曲半径设计的足够大,但这样会占用很多的版图面积。本文基于铌酸锂平台提出了一种可以同时测试波导传输损耗和弯曲损耗的方法。通过仿真发现波导弯曲损耗与弯曲半径成指数关系,对弯曲损耗取对数值后,与弯曲半径成线性关系。利用遗传算法拟合cut-back结构的插入损耗曲线,并计算得到波导的传输损耗和弯曲损耗。用该方法测量铌酸锂波导,在1550 nm波长下得到0.558 dB/cm的传输损耗和100μm弯曲半径下0.698 dB/90°的弯曲损耗。利用这种方法可以同时测试波导的传输损耗和弯曲损耗,还可以大大节省占地面积。
The propagation loss of a waveguide is a key indicator to evaluate the performance of an integrated optical platform.The commonly used cut-back method for measuring propagation loss requires the introduction of the spiral test structure.In order to remove bending loss,the bending radius is usually designed to be larger but this consequently has a larger footprint.In this paper,we suggested a method to simultaneously measure the propagation loss and bending loss of waveguides with a cut-back structure.According to simulations,the bending loss can be exponentially fitted with the bending radius,which can be further simplified as linear fitting between the natural logarithm of the bending loss and bending radius.A genetic algorithm was used to fit the insertion loss curve of the cut-back structure and the propagation losses and bending loss were calculated.With this method,we measured a cut-back structure of lithium niobate waveguide and got a propagation loss of 0.558 dB/cm and a bending loss of 0.698 dB/90°at a radius of 100μm and wavelength of 1550 nm.Using this method,we can simultaneously measure waveguide propagation loss and bending loss while mitigating the footprint.
作者
范作文
贾连希
李赵一
周敬杰
丛庆宇
曾宪峰
FAN Zuo-wen;JIA Lian-xi;LI Zhao-yi;ZHOU Jing-jie;CONG Qing-yu;ZENG Xian-feng(Microelectronics Institute,Shanghai University,Shanghai 201800,China;Shanghai Institute of Microsystems and Information Technology,Chinese Academy of Sciences,Shanghai 201800,China;Shanghai IndustrialμTechnology Research Institute,Shanghai 201800,China)
出处
《中国光学(中英文)》
EI
CAS
CSCD
北大核心
2023年第5期1177-1185,共9页
Chinese Optics
基金
国家重点研发计划(No.2018YFB2200500)。
关键词
传输损耗
弯曲损耗
铌酸锂
遗传算法
propagation loss
bending loss
lithium niobate
genetic algorithm