Transient Modeling of High-Speed Links Using Transfer Learning-Based Neural Network Initialization

Accurate and efficient signal integrity modeling methods are crucial in the iterative design process of high-speed links. While data-driven deep learning exhibits robust capabilities for temporal transient modeling, it often ignores the correlations among high-speed links sharing similar structures, necessitating separate retraining for each distinct case. In this study, we propose a new transient modeling approach for high-speed links employing a transfer learning-enhanced deep simple recurrent unit (TL-DSRU) method. The deep simple recurrent unit architecture overcomes the challenges in handling sequential data and parallel processing found in traditional recurrent neural networks, enabling efficient modeling. Our technique leverages the initialization principles of transfer learning, utilizing a pretrained model of a basic high-speed link to enhance the comprehension of more intricate cases. The proposed TL-DSRU model combines parallelization and transfer learning initialization methods to balance training speed and accuracy, thereby enhancing the practicality and generalization potential of neural network-based transient simulation for high-speed links. Comparative experiments demonstrate that the transfer learning-based initialization method substantially outperforms typical neural network random initialization techniques, delivering markedly improved time-domain waveform prediction accuracy across various channels and equalization in high-speed links, as well as yielding more precise predictions of eye diagram parameters.