Coherent Supercontinuum Generation in Step-Index Heavily Ge-Doped Silica Fibers with All Normal Dispersion
IEEE photonics journal（2022）
The Key Laboratory of Material Science for High Power Laser
Abstract
The step-index heavily germanium-doped silica fibers with all normal dispersion (ANDi) are promising candidates for highly coherent supercontinuum (SC). These ANDi step-index silica fibers are easier to fabricate, splice and handle than silica photonic crystal fibers (PCFs). 40% GeO2 doped step-index silica fiber of 3 μm core diameter has flat and near-zero dispersion which is between −5.5 and 0 ps/nm/km from 1.4 to 2.4 μm. Highly coherent SC spanning from 1050 nm to 2100 nm generated from the ANDi step-index heavily germanium-doped silica fiber pumped at 1560 nm. All-fiber coherent SC source from 1200 nm to 2200 nm is achieved by splicing the ANDi silica fiber with a 1560 nm femtosecond fiber laser of 90% couple efficiency. Moreover, the step-index ANDi germania-core silica fiber of 2.2 μm core diameter is proposed to generate coherent mid-infrared SC from 1.7 to 3 μm. The ANDi step-index silica fibers not only can generate highly coherent broadband SC at near infrared or mid-infrared region but also can easily achieve all-fiber structure coherent SC source. And the experiment of supercontinuum generation in UNNA4 and UHNA7 fibers with different pump wavelengths indicates that the pump of 1064 nm is not suitable for coherent supercontinuum generation in the fiber of UNNA4 and UHNA7.
MoreTranslated text
Key words
Supercontinuum,coherent,normal dispersion
求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined