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Inverse Design of Tunable Graphene-Based Terahertz Metasurfaces via Deep Neural Network and SHADE Algorithm.

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Titel: Inverse Design of Tunable Graphene-Based Terahertz Metasurfaces via Deep Neural Network and SHADE Algorithm.
Autoren: Chen, Siyu, Lin, Junyi, Sun, Jingchun, Li, Xue-Shi
Quelle: Photonics; Sep2025, Vol. 12 Issue 9, p910, 25p
Schlagwörter: TERAHERTZ technology, GRAPHENE, PREDICTION models, SPECTRUM analysis, DESIGN techniques, METAHEURISTIC algorithms, NANOSTRUCTURES, ARTIFICIAL neural networks
Abstract: The terahertz (THz) frequency range holds critical importance for next-generation, wireless communications and biomedical sensing applications. However, conventional metamaterial design approaches suffer from computationally intensive simulations and optimization processes that can extend over several months. This work presents an intelligent inverse design framework integrating deep neural network (DNN) surrogate modeling with success-history-based adaptive differential evolution (SHADE) for tunable graphene-based THz metasurfaces. Our DNN surrogate model achieves an exceptional coefficient of determination (R2 = 0.9984) while providing a four-order-of-magnitude acceleration compared with conventional electromagnetic solvers. The SHADE-integrated framework demonstrates 96.7% accuracy in inverse design tasks with an average convergence time of 10.2 s. The optimized configurations exhibit significant tunability through graphene Fermi level modulation, as validated by comprehensive electromagnetic field analysis. This framework represents a significant advancement in automated electromagnetic design and establishes a robust foundation for intelligent photonic systems across diverse frequency regimes. [ABSTRACT FROM AUTHOR]
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Datenbank: Complementary Index
Beschreibung
Abstract:The terahertz (THz) frequency range holds critical importance for next-generation, wireless communications and biomedical sensing applications. However, conventional metamaterial design approaches suffer from computationally intensive simulations and optimization processes that can extend over several months. This work presents an intelligent inverse design framework integrating deep neural network (DNN) surrogate modeling with success-history-based adaptive differential evolution (SHADE) for tunable graphene-based THz metasurfaces. Our DNN surrogate model achieves an exceptional coefficient of determination (R<sup>2</sup> = 0.9984) while providing a four-order-of-magnitude acceleration compared with conventional electromagnetic solvers. The SHADE-integrated framework demonstrates 96.7% accuracy in inverse design tasks with an average convergence time of 10.2 s. The optimized configurations exhibit significant tunability through graphene Fermi level modulation, as validated by comprehensive electromagnetic field analysis. This framework represents a significant advancement in automated electromagnetic design and establishes a robust foundation for intelligent photonic systems across diverse frequency regimes. [ABSTRACT FROM AUTHOR]
ISSN:23046732
DOI:10.3390/photonics12090910