Semi-supervised Regression Model for Eye Diagram Estimation of High Bandwidth Memory (HBM) Silicon Interposer
- Resource Type
- Conference
- Authors
- Li, Guo-Sheng; Mao, Chang-Sheng; Zhao, Wen-Sheng
- Source
- 2023 International Applied Computational Electromagnetics Society Symposium (ACES-China) Applied Computational Electromagnetics Society Symposium (ACES-China), 2023 International. :1-3 Aug, 2023
- Subject
- Engineering Profession
Fields, Waves and Electromagnetics
Training
Convolution
Stripline
Estimation
Channel estimation
Bandwidth
Artificial neural networks
eye diagram
signal integrity
deep neural network (DNN)
convolutional neural network (CNN)
- Language
This paper presents a semi-supervised regression model for accurate eye height and eye width estimation of high bandwidth memory (HBM) silicon interposer channels. The model combines a channel characteristic-based deep neural network with a co-trained convolution neural network. For verification, the proposed model was applied to the stripline channels of an HBM silicon interposer. The root-mean-square errors (RMSE) of the predicted results of eye width and eye height are 4.95 mV and 7.32 ps.