A Fast Signal Integrity Design Model of Printed Circuit Board based on MonteCarlo Tree In this paper, we discuss the signal integrity of the printed circuit board(PCB) and put forward a new method of designing PCB parameters based on montecarlo tree search(MCTS). First, effects and measurements of the signal integrity are analysed and presented. Next, the paper introduces the algorithm MCTS on how to conduct parameters assignments and presents the improved upper confidence bound(UCB) algorithm that adapts to the PCB parameter design. We simulate and collect eye metrics data on the software and accelerate the process of designing a signal integrated PCB by employing MCTS. Compared to randomly searching for optimal parameters combination, the method generates an optimal global consequence rather than working out a suboptimum result. The total time cost is within ten minutes, while the random search takes hours to get the result.

A DataEfficient Training Model for Signal Integrity Analysis based on Transfer Learning The signal integrity analysis is an essential part of electronic design, while direct high speed signal analysis becomes a timeconsuming work. As machine learning exhibits high performance in communication fields in recent years, deep neural network(DNN) is thought to be a key tool to predict eye diagram metrics. However, DNN based signal integrity analysis faces two challenges: demands for amounts of labelled data and long training period. In this paper, we propose a signal integrity analysis model based on transfer learning. The model makes full use of a trained network and trains networks for various channel environments. To achieve the same predicting accuracy, 64% labelled data are utilized for training compared to DNN. The application of hyperparameters in the neural network improves the prediction accuracy of the eye diagram width and height by 42.7% and 49.24% compared to current methods in few shot signal integrity analysis.