Optimized Design of Terahertz Coding Frequency Selection Surface Based on Deep Learning
This study proposes an intelligent integration-design method to address the problems of complex structure,numerous parameters,and time-consuming optimization of terahertz frequency selective surface(FSS)cells.The method is based on convolutional neural network(CNN)combined with genetic algorithm,and is applied to the performance optimization design of typical filtered FSS.The FSS periodic cell topology is encoded as a 16×16"0/1"rotationally symmetric sequence,and 26,000 groups of transmission and reflectance spectra in the range of 0.5‒3 THz are collected as the dataset.A 19-layer CNN is used to obtain the spectral prediction with an average absolute error as low as 0.06 on the test set.The difference between the predicted and the target spectra is used to give a generalized objective function for the design of various typical FSS cells.Combined with the genetic algorithm,a single-frequency bandpass and bandstop FSSs with a bandwidth of 0.1 THz,a single-frequency bandpass and bandstop FSSs with a bandwidth of 0.5 THz,and a dual-frequency bandpassFSS with a bandwidth of 0.2 THz are designed and implemented,with good polarization stability for all of them.Computational results show that various typical bandpass and bandstop FSS cells can be realized concisely and efficiently by optimizing their topological coding.
frequency selective surfacetopological codingconvolutional neural networkgenetic algorithm
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