Deep Learning: A Rapid and Efficient Route to Automatic Metasurface Design

By Tianshuo Qiu, Xin Shi, Jiafu Wang, Yongfeng Li, S. Qu, Qiang Cheng, T. Cui, S. Sui. 2019

365 citations recorded by the source corpus.

Metasurfaces provide unprecedented routes to manipulations on electromagnetic waves, which can realize many exotic functionalities. Despite the rapid development of metasurfaces in recent years, the design process of metasurface is still time‐consuming and computational resource‐consuming. Moreover, it is quite complicated for layman users to design metasurfaces as plenty of specialized knowledge is required. In this work, a metasurface design method named REACTIVE is proposed on the basis of deep learning, as deep learning method has shown its natural advantages and superiorities in mining undefined rules automatically in many fields. REACTIVE is capable of calculating metasurface structure directly through a given design target; meanwhile, it also shows the advantage in making the design process automatic, more efficient, less time‐consuming, and less computational resource‐consuming. Besides, it asks for less professional knowledge, so that engineers are required only to pay attention to the design target. Herein, a triple‐band absorber is designed using the REACTIVE method, where a deep learning model computes the metasurface structure automatically through inputting the desired absorption rate. The whole design process is achieved 200 times faster than the conventional one, which convincingly demonstrates the superiority of this design method. REACTIVE is an effective design tool for designers, especially for laymen users and engineers.

Subfields: Aerospace Engineering, Electronic, Optical and Magnetic Materials

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Citations in the corpus

How to See Hidden Patterns in Metamaterials with Interpretable Machine Learning (2021, background): “[8] Tianshuo Qiu, Xin Shi, Jiafu Wang, Yongfeng Li, Shaobo Qu, Qiang Cheng, Tiejun Cui, Sai Sui, Deep learning: A rapid and efficient route to automatic metasurface design, Adv.”
RUN beyond the metaphor: An efficient optimization algorithm based on Runge Kutta method (2021, background): “Real-world problems are faced every day, and we need to develop solvers for deep learning applications (Chen, Chen, et al., 2020; Li, et al., 2019; Qiu, et al., 2019), decision-making procedures (Liu, et al., 2016; Liu, et al.; Wu, et al., 2020), optimal resource allocation (Yan, et al., 2020),…”
Interfacing photonics with artificial intelligence: an innovative design strategy for photonic structures and devices based on artificial neural networks (2021, methodology): “In parallel, T. Qiu et al. proposed a new method, named REACTIVE, to conduct the inverse design based on reflection spectra [39].”
Metantenna: When Metasurface Meets Antenna Again (2020, methodology): “To tackle these problems, one of the most efficient methods is to develop fast design methods based on genetic algorithm, machine learning, and so on [35], [36].”