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Reconstructing the Semiconductor Band Structure by Deep Learning

Author

Listed:
  • Shidong Yang

    (Institute of Mathematics, College of Science, Shantou University, Shantou 515063, China)

  • Xiwang Liu

    (School of Science and Center for Theoretical Physics, Hainan University, Haikou 570288, China)

  • Jinyan Lin

    (Research Center for Advanced Optics and Photoelectronics, Department of Physics, College of Science, Shantou University, Shantou 515063, China)

  • Ruixin Zuo

    (Institute of Mathematics, College of Science, Shantou University, Shantou 515063, China
    Department of Physics and CeOPP, Universität Paderborn, Warburger Strasse 100, D-33098 Paderborn, Germany)

  • Xiaohong Song

    (School of Science and Center for Theoretical Physics, Hainan University, Haikou 570288, China)

  • Marcelo Ciappina

    (Physics Program, Guangdong Technion—Israel Institute of Technology, Shantou 515063, China
    Technion—Israel Institute of Technology, Haifa 32000, Israel
    Guangdong Provincial Key Laboratory of Materials and Technologies for Energy Conversion, Guangdong Technion—Israel Institute of Technology, Shantou 515063, China)

  • Weifeng Yang

    (School of Science and Center for Theoretical Physics, Hainan University, Haikou 570288, China)

Abstract

High-order harmonic generation (HHG), the nonlinear upconversion of coherent radiation resulting from the interaction of a strong and short laser pulse with atoms, molecules and solids, represents one of the most prominent examples of laser–matter interaction. In solid HHG, the characteristics of the generated coherent radiation are dominated by the band structure of the material, which configures one of the key properties of semiconductors and dielectrics. Here, we combine an all-optical method and deep learning to reconstruct the band structure of semiconductors. Our method builds up an artificial neural network based on the sensitivity of the HHG spectrum to the carrier-envelope phase (CEP) of a few-cycle pulse. We analyze the accuracy of the band structure reconstruction depending on the predicted parameters and propose a prelearning method to solve the problem of the low accuracy of some parameters. Once the network is trained with the mapping between the CEP-dependent HHG and the band structure, we can directly predict it from experimental HHG spectra. Our scheme provides an innovative way to study the structural properties of new materials.

Suggested Citation

  • Shidong Yang & Xiwang Liu & Jinyan Lin & Ruixin Zuo & Xiaohong Song & Marcelo Ciappina & Weifeng Yang, 2022. "Reconstructing the Semiconductor Band Structure by Deep Learning," Mathematics, MDPI, vol. 10(22), pages 1-11, November.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:22:p:4268-:d:973239
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    References listed on IDEAS

    as
    1. T. T. Luu & M. Garg & S. Yu. Kruchinin & A. Moulet & M. Th. Hassan & E. Goulielmakis, 2015. "Extreme ultraviolet high-harmonic spectroscopy of solids," Nature, Nature, vol. 521(7553), pages 498-502, May.
    2. M. Hohenleutner & F. Langer & O. Schubert & M. Knorr & U. Huttner & S. W. Koch & M. Kira & R. Huber, 2015. "Real-time observation of interfering crystal electrons in high-harmonic generation," Nature, Nature, vol. 523(7562), pages 572-575, July.
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