Meander Structure Analysis Techniques Using Artificial Neural Networks

Abstract

Typically, analyses of meander structures (MSs) for transfer characteristics are conducted using specialized commercial software based on numerical methods. However, these methods can be time-consuming, particularly when a researcher is seeking to perform a preliminary study of the designed structures. This study aims to explore the application of neural networks in the design and analysis of meander structures. Three different feedforward neural network (FFNN), time delay neural network (TDNN), and convolutional neural network (CNN) techniques were investigated for the analysis and design of the meander structures in this article. The geometric dimensions or top-view images of 369 different meander structures were used for training an FFNN, TDNN, and CNN. The investigated networks were designed to predict such electrodynamic parameters as the delay time (td), reflection coefficient (S11), and transmission coefficient (S21) in the 0–10 GHz frequency band. A sufficiently low mean absolute error (MAE) was achieved with all three methods for the analysis of MSs. Using an FFNN, the characteristic td was predicted with a 3.3 ps average MAE. The characteristic S21 was predicted with a 0.64 dB average MAE, and S11 was predicted with a 2.47 dB average MAE. The TDNN allowed the average MAEs to be reduced to 0.9 ps, 0.11 dB, and 1.63 dB, respectively. Using a CNN, the average MAEs were 27.5 ps, 0.44 dB, and 1.36 dB, respectively. The use of neural networks has allowed accelerating the analysis procedure from approximately 120 min on average to less than 5 min.