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Chin. Phys. B, 2021, Vol. 30(4): 048402    DOI: 10.1088/1674-1056/abf0ff

Convolutional neural network for transient grating frequency-resolved optical gating trace retrieval and its algorithm optimization

Siyuan Xu(许思源)1,2, Xiaoxian Zhu(朱孝先)2,3, Ji Wang(王佶)2,4, Yuanfeng Li(李远锋)1,2, Yitan Gao(高亦谈)2,3, Kun Zhao(赵昆)2,5,†, Jiangfeng Zhu(朱江峰)1,‡, Dacheng Zhang(张大成)1, Yunlin Chen(陈云琳)4, and Zhiyi Wei(魏志义)2,3,5
1 School of Physics and Optoelectronic Engineering, Xidian University, Xi'an 710071 China; 2 Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, Beijing 100190, China; 3 University of Chinese Academy of Sciences, Beijing 100049, China; 4 Institute of Applied Micro-Nano Materials, School of Science, Beijing Jiaotong University, Beijing 100044, China; 5 Songshan Lake Material Laboratory, Dongguan 523808, China
Abstract  A convolutional neural network is employed to retrieve the time-domain envelop and phase of few-cycle femtosecond pulses from transient-grating frequency-resolved optical gating (TG-FROG) traces. We use theoretically generated TG-FROG traces to complete supervised trainings of the convolutional neural networks, then use similarly generated traces not included in the training dataset to test how well the networks are trained. Accurate retrieval of such traces by the neural network is realized. In our case, we find that networks with exponential linear unit (ELU) activation function perform better than those with leaky rectified linear unit (LRELU) and scaled exponential linear unit (SELU). Finally, the issues that need to be addressed for the retrieval of experimental data by this method are discussed.
Keywords:  transient-grating frequency-resolved optical gating      convolutional neural network      activation function      phase retrieval algorithm  
Received:  24 February 2021      Revised:  03 March 2021      Accepted manuscript online:  23 March 2021
PACS:  84.37.+q (Measurements in electric variables (including voltage, current, resistance, capacitance, inductance, impedance, and admittance, etc.))  
  84.35.+i (Neural networks)  
  07.05.Pj (Image processing)  
  42.65.Hw (Phase conjugation; photorefractive and Kerr effects)  
Fund: Project supported by the National Key R&D Program of China (Grant No. 2017YFB0405202) and the National Natural Science Foundation of China (Grant Nos. 61690221, 91850209, and 11774277).
Corresponding Authors:  Corresponding author. E-mail: Corresponding author. E-mail:   

Cite this article: 

Siyuan Xu(许思源), Xiaoxian Zhu(朱孝先), Ji Wang(王佶), Yuanfeng Li(李远锋), Yitan Gao(高亦谈), Kun Zhao(赵昆), Jiangfeng Zhu(朱江峰), Dacheng Zhang(张大成), Yunlin Chen(陈云琳), and Zhiyi Wei(魏志义) Convolutional neural network for transient grating frequency-resolved optical gating trace retrieval and its algorithm optimization 2021 Chin. Phys. B 30 048402

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