Highly Birefringent Anti-Resonant Hollow-Core Fiber with a Low Loss
IEEE PHOTONICS TECHNOLOGY LETTERS（2023）
Zhejiang Univ
Abstract
In recent years, anti-resonant hollow-core fibers (known as AR-HCFs) have gradually become one of the research hotspots. However, their loss and birefringence properties are often difficult to reconcile. In this letter, a new structure of AR-HCF is proposed by analyzing the factors that affect their loss and birefringence. The fiber features a negative curvature core surround, double-layer structures, and two half-tubes containing nested tubes. Based on COMSOL Multiphysics, the designed AR-HCF has a high birefringence of $1.3\times 10 ^{-4}$ and a low confinement loss of 6.1 dB $\cdot $ km−1 at 1550 nm, a bandwidth of 40 nm, bending resistance and single-mode operation.
MoreTranslated text
Key words
High birefringence,low confinement loss,double-layer,half-tube,AR-HCF
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
Related Papers
2013
被引用290 | 浏览
2015
被引用55 | 浏览
2020
被引用11 | 浏览
2018
被引用17 | 浏览
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
GPU is busy, summary generation fails
Rerequest