光子晶体光纤
色散(光学)
材料科学
波长
四波混频
光学
混合(物理)
反向
人工神经网络
信号(编程语言)
激光器
计算机科学
光电子学
非线性光学
物理
数学
人工智能
量子力学
程序设计语言
几何学
作者
Linqiao Gan,Fei Yu,Yazhou Wang,Ning Wang,Xinyue Zhu,Lili Hu,Chunlei Yu
出处
期刊:Photonics
[MDPI AG]
日期:2023-03-10
卷期号:10 (3): 294-294
被引量:2
标识
DOI:10.3390/photonics10030294
摘要
In this paper, we demonstrate the application of a deep learning neural network (DNN) in the dispersion-oriented inverse design of photonic-crystal fiber (PCF) for the fine-tuning of four-wave mixing (FWM). The empirical formula of PCF dispersion is applied instead of numerical simulation to generate a large dataset of phase-matching curves of various PCF designs, which significantly improves the accuracy of the DNN prediction. The accuracies of DNNs’ predicted PCF structure parameters are all above 95%. The simulations of the DNN-predicted PCFs structure demonstrate that the FWM wavelength has an average numerical mean square error (MAE) of 1.92 nm from the design target. With the help of DNN, we designed and fabricated a specific PCF for wavelength conversion via FWM from 1064 nm to 770 nm for biomedical imaging applications. Pumped by a microchip laser at 1064 nm, the signal wavelength is measured at 770.2 nm.
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