Name
Title | ||
|---|---|---|
Semi-Supervised Learning Based on Hybrid Neural Network for the Signal Integrity Analysis |
Abstract | ||
|---|---|---|
The signal integrity analysis of high-speed circuit channels becomes a challenging task, with the development of integrated circuit technology. To solve this problem, we proposed a fast-training semi-supervised learning method based on hybrid neural network (HNN) to predict the eye-diagram metrics. Compared with the existing methods, the proposed method only requires a small amount of training data with labels, the proposed method can automatically generate the labels for the unlabeled data with a small amount of labeled data with HNN based semi-supervised learning. To this end, the proposed method can save a great amount of time, which will be a more realistic solution for the practical application. Compared with existing machine learning-based methods, the proposed method requires 50% less labeled data for training with 32.29% and 20.73% accuracy improving on deep neural network (DNN) and co-training-style semi-supervised regression (COREG) methods, receptively. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/TCSII.2019.2948527 | IEEE Transactions on Circuits and Systems II: Express Briefs |
Keywords | DocType | Volume |
Signal integrity,Neural networks,Training,Semisupervised learning,Euclidean distance,Training data | Journal | 67 |
Issue | ISSN | Citations |
10 | 1549-7747 | 1 |
PageRank | References | Authors |
0.36 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Siyu Chen | 1 | 1 | 0.36 |
| Jienan Chen | 2 | 84 | 13.64 |
| Tingrui Zhang | 3 | 1 | 0.36 |
| Shuwu Wei | 4 | 1 | 0.36 |