Design of frequency selective surface based on reflection coefficients in C and X bands using nested convolutional neural network

Frequency selective surfaces (FSS) are surfaces consisting of periodically placed shapes used to reflect, transmit and absorb electromagnetic waves. FSS has many uses such as increasing the antenna performance, reducing the radar cross section, filtering, and increasing the photovoltaic cell performance. Due to the use of FSS in many fields, it has attracted the attention of researchers and has led to studies in this field. The studies are generally aimed at obtaining the FSS surface with optimization algorithms and methods. For this purpose, the desired reflection coefficients (S11) are obtained by iteration. With the significant increase in computational power in recent years, the use of machine learning algorithms has become more widespread and successful results have been obtained in classification and regression problems. In this thesis, studies were carried out to obtain the FSS corresponding to the desired S11 with Decision trees, K-nearest neighbor, Deep learning and hybrid deep learning algorithms from machine learning algorithms. In the training process of machine learning algorithms, reflection coefficients between 6-14 GHz were used as input, while 50x50 FSS images were used as targets. Machine learning models were trained using 29722 reflection coefficients in total. As a result of the training process, the most successful result was obtained with the nested convolutional neural network model, which is a hybrid model. The mean absolute error value of the nested convolutional neural network model was 0.69, and the R2 value was 0.9.