## 6 Conclusions  
In this paper, a DNN-Transformer cascaded neural network is proposed for effectively analyzing the SI in high-speed serial links. During the dataset creation process, we referenced the USB4 Gen4 and 50 GBASE-KR standards for PCB design and electromagnetic simulation, and used the physical design parameters of each channel as inputs for the model. This DNN-Transformer model is used in this paper to extract the features from physical design parameters of channels and successfully predict the eye diagram data and COM values of test links. In addition, this deep learning model can successfully predict the SBR before and after equalization, and main equalizer parameters for different combinations are also accurately predicted. For the model's training, we employed a Bayesian optimization method based on the GP for HPO. Finally, this paper compares DNN-Transformer with various other models such as DNN, LSTM, Transformer, and DNN-LSTM models. The results shows that our DNN-Transformer cascaded model accurately achieves performance prediction and equalization architecture optimization for high-speed serial links, and the MRE in its COM prediction results for the test set, with an equalizer configuration comprising a 3-taps TX FFE, an RX CTLE with dual DC gain, and a 12-taps RX DFE, is 1.6%.  
Conceptualization, L.W., J.Z. and Y.Z. ([PERSON]); methodology, L.W., J.Z. and H.J.; software, L.W.; validation, L.W., J.Z. and Y.Z. ([PERSON]); formal analysis, L.W., H.J. and Y.Z. ([PERSON]); investigation, L.W.; resources, L.W., Y.Z. ([PERSON]) and Y.Z. ([PERSON]); data curation, L.W. and Y.Z. ([PERSON]); writing--original draft preparation, L.W.; writing--review and editing, X.Y., Y.Z. ([PERSON]) and Y.Z. ([PERSON]); visualization, L.W.; supervision, X.Y., Y.Z. ([PERSON]) and [PERSON] ([PERSON]); project administration, L.W.; funding acquisition, L.W., Y.Z. ([PERSON]) and X.Y. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (Grant No. 61861019, 62161012), the Hunan Provincial Department of Education (Grant No. 22B0525, 21A0335),  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & **Physical Parameters** & **Transient Waveform** & **FD Prediction** & **Link Optimization** & **GPU/CPU Time (ms)** & **RMSE** & **MAE** \\ \hline SVM [13] & Yes & No & No & No & \textless{5} * & \(3.8\times 10^{-1}\) & \(3.1\times 10^{-1}\) \\ \hline RFR [12] & Yes & No & Yes & No & \textless{5} * & \(4.8\times 10^{-1}\) & \(3.8\times 10^{-1}\) \\ \hline DNN [9] & Yes & No & No & No & \textless{5} & \(6.9\times 10^{-2}\) & \(4.3\times 10^{-2}\) \\ \hline LSTM [20] & No & Yes & No & No & \textless{5} & \(5.3\times 10^{-2}\) & \(4.4\times 10^{-2}\) \\ \hline GNN-RNN [21] & Yes & Yes & No & No & 567.1 & \(\backslash\) & \(\backslash\) \\ \hline DNN–Transformer & Yes & Yes & Yes & Yes & 792.8 * & \(4.6\times 10^{-2}\) & \(3.5\times 10^{-2}\) \\ \hline \hline \end{tabular}  
* GPU inference time.  
\end{table}
Table 7: Functional comparison of different models for high-speed links.  
the China Postdoctoral Science Foundation (Grant No. 2024M751268), and the Postgraduate Research Program of Jishou University (Grant No. Jdy23035).  
**Data Availability Statement:** The data used to support the findings of the study are available within the article.  
**Conflicts of Interest:** Author [PERSON] was employed by the company Shandong Yunhai Guochuang Cloud Computing Equipment Industry Innovation Company Limited. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.