Predicting the Characteristics of High-Speed Serial Links Based on a Deep Neural Network (DNN)--Transformer Cascaded Model  
[PERSON]  
1 School of Communication and Electronic Engineering, Jishou University, Jishou 416000, China; [EMAIL_ADDRESS] (L.W. ); [EMAIL_ADDRESS] (J.Z. ); [EMAIL_ADDRESS] (H.J. ); 1 [EMAIL_ADDRESS] (X.Y)  
[PERSON]  
1 School of Communication and Electronic Engineering, Jishou University, Jishou 416000, China; [EMAIL_ADDRESS] (L.W. ); [EMAIL_ADDRESS] (J.Z. ); [EMAIL_ADDRESS] (J.Z. ); [EMAIL_ADDRESS] (H.J. ); 1 [EMAIL_ADDRESS] (X.Y)  
[PERSON]  
1 School of Communication and Electronic Engineering, Jishou University, Jishou 416000, China; [EMAIL_ADDRESS] (L.W. ); [EMAIL_ADDRESS] (J.Z. ); [EMAIL_ADDRESS] (J.Z. ); [EMAIL_ADDRESS] (H.J. ); 1 [EMAIL_ADDRESS] (X.Y)  
[PERSON]  
1 School of Communication and Electronic Engineering, Jishou University, Jishou 416000, China; [EMAIL_ADDRESS] (L.W. ); [EMAIL_ADDRESS] (J.Z. ); [EMAIL_ADDRESS] (J.Z. ); [EMAIL_ADDRESS] (H.J. ); [EMAIL_ADDRESS]. discards the channel margins due to the need to strictly satisfy discrete metrics [4]. On the contrary, the channel operating margin (COM) technique can effectively overcome this disadvantage by searching the optimal design space of the whole link in the form of a signal-to-noise ratio (SNR). Compared to the traditional SI metrics such as eye diagrams and the bit error rate (BER), the COM approach shows advantages such as simpler operation, faster speed, and more efficient testing. Obviously, compared with Annex69B channel evaluation for 10 Gb/s Ethernet (10 GbE), using COMs is more accurate [5]. Although the COM approach can provide an effective way to evaluate and optimize high-speed links, it requires numerous iterations for spatial search and is not flexible enough to analyze high-capacity channels. Machine learning (ML) has been widely used in the simulation and design of high-speed links in recent years, and shows the ability to improve the efficiency of SI analysis. The authors of [4; 6; 7; 8; 9; 10] searched for features from simulation data and trained Artificial Neural Network (ANN), Deep Neural Network (DNN), and Least-Squares Support Vector Machine (LS-SVM) models to replace circuit system simulations for the accurate prediction of time-domain (TD) and frequency-domain metrics such as eye height (EH)/eye width (EW) and return loss (RL)/insertion loss (IL); however, the acquisition of simulation data consumes a lot of computational resources and time. Feedforward Neural Networks (FNNs), Random Forest Regression (RFR), and Support Vector Machines (SVMs) are used to achieve simple predictions of simulation data [11; 12; 13], such as for predicting S-parameters and impulse responses. When dealing with more complex simulation data, traditional ML methods may lose many features. The Recurrent Neural Network (RNN) is an ML model that can effectively capture the features of complex data, especially for sequence data such as S-parameters and impulse responses. The authors of [14; 15; 16; 17; 18; 19; 20] employ RNN and Long Short-Term Memory (LSTM) architecture to create surrogate models for predicting the transient response waveforms of complex high-speed links. These independent surrogate models can perform SI analysis based on simulation data, but cannot deal with the physical parameters of links. SI analysis based on physical parameters requires more complicated ML methods, and the authors of [21; 22; 23; 24] achieved effective predictions of physical parameters used for assessing high-speed link performance by combining multiple deep learning algorithms. Moreover, balanced architecture optimization for high-speed serial links can be achieved based on ML [4; 25; 26; 27]. In conclusion, performance prediction and architecture optimization [28] are two important parts of ML applications for high-speed serial links. In this paper, a DNN-Transformer cascaded model is proposed for the SI analysis and optimization of high-speed serial links.