Artificial Neural Network For Crosstalk Prediction In Stripline Transmission Lines - Kong, Chun Lei (Universiti Sains Malaysia, 2018)
Crosstalk can cause serious electromagnetic interference problem and crosstalk prediction in the early design stage is important. Several conventional modeling methods such as RDSI and SPICE have previously presented to predict crosstalk in non-uniform transmission lines and it needs large CPU memory consumption and long simulation time. DOE is applied to efficiently select training data and reduce the number of EM simulations in the Advanced Design System (ADS). Momentum EM Simulator is used to extract S-parameters from coupled stripline with different design parameters and generated an efficient dataset. Matlab Neural Network Toolbox is used to create neural network models. Neural network models are trained to learn the characterization and behavior of data for crosstalk estimation in stripline. Lastly, the neural model is validated by comparing the simulated results and predicted results from ADS and ANN. The performance evaluation shows that the crosstalk prediction in stripline achieved 99.9% with training time of 0.2810s. In conclusion, this verified that the ANN is effective in the stripline crosstalk prediction.