Recurrent Neural Network (RNN) Based Signal Integrity Assessment for Coaxial-Through Glass Vias in Three-Dimensional Integration

In the realm of high-frequency three-dimensional integrated circuits, performance of through-glass vias (TGVs) is of significant concern. Through recurrent neural network (RNN) technique, this paper presents an innovative approach of signal integrity analysis for coaxial-TGVs. This technique leverages the temporal dependencies of electromagnetic fields within coaxial-TGVs and effectively captures the intricate interactions that cause crosstalk. By training the RNN on the TGV dataset, a more accurate and adaptable crosstalk prediction in coaxial-TGVs has been achieved.