Yang Ruitao, Wu Jinxuan, Yang Hongxing, Fu Haijin, Yu Liang, Xing Xu, Dong Yisi, Hu Pengcheng, Tan Jiubin
Center of Ultra-Precision Optoelectronic Instrument Engineering, Harbin Institute of Technology, Harbin 150080, China.
Key Lab of Ultra-Precision Intelligent Instrumentation, Harbin Institute of Technology, Ministry of Industry and Information Technology, Harbin 150080, China.
Nanomaterials (Basel). 2022 Nov 5;12(21):3907. doi: 10.3390/nano12213907.
Cavity-enhanced electro-optic comb generators (CEEOCGs) can provide optical frequency combs with excellent stability and configurability. The existing methods for CEEOCGs spectrum characterization, however, are based on approximations and have suffered from either iterative calculations or limited applicable conditions. In this paper, we show a spectrum characterization method by accumulating the optical electrical field with respect to the count of the round-trip propagation inside of CEEOCGs. The identity transformation and complete analysis of the intracavity phase delay were conducted to eliminate approximations and be applicable to arbitrary conditions, respectively. The calculation efficiency was improved by the noniterative matrix operations. Setting the maximum propagation count as 1000, the spectrum of the center ±300 comb modes can be characterized with merely the truncation error of floating-point numbers within 1.2 s. More importantly, the effects of all CEEOCG parameters were comprehensively characterized for the first time. Accordingly, not only the exact working condition of CEEOCG can be identified for further optimization, but also the power of each comb mode can be predicted accurately and efficiently for applications in optical communications and waveform synthesis.
腔增强电光梳状发生器(CEEOCGs)能够提供具有出色稳定性和可配置性的光学频率梳。然而,现有的CEEOCGs频谱表征方法基于近似,存在迭代计算或适用条件有限的问题。在本文中,我们展示了一种通过累积CEEOCGs内部往返传播次数的光电场来进行频谱表征的方法。分别进行了腔内相位延迟的恒等变换和完整分析,以消除近似并适用于任意条件。通过非迭代矩阵运算提高了计算效率。将最大传播次数设置为1000,在1.2秒内仅需考虑浮点数的截断误差,即可表征中心±300梳状模式的频谱。更重要的是,首次全面表征了所有CEEOCG参数的影响。因此,不仅可以确定CEEOCG的精确工作条件以进行进一步优化,还可以准确高效地预测每个梳状模式的功率,以应用于光通信和波形合成。