Inverse Design of a Terahertz Metasurface with Split Ring Resonator Based on Deep Learning

Designing a metasurface structure on demand is an extremely time-consuming process. As an efficient machine learning method, deep learning has been widely used for data classification and regression in recent years and in fact shown good generalization performance. We have built a deep neural network for ondemand design of a terahertz (THz) metasuface with split ring resonator. With the required reflectance as input, the parameters of the structure are automatically calculated and then output to achieve the purpose of designing on demand. The results indicate that using deep learning to train the data, the trained model can more accurately guide the design of the structure, thereby speeding up the design process. Compared with the traditional design process, using deep learning to guide the design of metasurface can achieve faster, more accurate, and more convenient purposes.