Inverse design of nanohole all-dielectric metasurface based on deep convolutional neural network.

Compared with the traditional micro-nano device design method, the data-driven neural network model method can greatly improve the inverse design efficiency of micro-nano structures with tiny design errors. Micro-nano structures based on the Fano resonance effect are widely used and have various functions. In recent years, researchers have attached great importance to its inverse design. This paper addresses the application requirements of the Fano resonance effect and develops a Gramian angular field-Convolutional Neural Network-Long Short Term Memory (GAF–CNN–LSTM) hybrid model based on the Gramian angular field transition. The model takes the designed nanohole all-dielectric metasurface structure as an example. This structure can produce different types of Fano resonance effects under different parameter combinations, implement different types of Fano resonances relative to multiple structures,the proposed structure can be realized by dynamic tuning of a single structure to obtain multi-Fano resonances, thus better application to different sensors.Through the ablation experiments and the contrast experiments, the results indicate that the proposed hybrid model can achieve high-precision inverse prediction of multifunctional Fano resonance effects, with a model accuracy of up to 95%. This study provides a new perspective on the inverse design of array-type multifunctional Fano resonance effect micro-nano devices. • The gramian angular field transition method is applied for the first time to the inverse design of metasurface structures. • A nanohole all-dielectric metasurface structure with multifunctional Fano resonance effect is proposed. • The proposed method achieves high-precision inverse design of the proposed metasurface structures with 95% model accuracy. [ABSTRACT FROM AUTHOR]