Channel Characteristic-Based Deep Neural Network Models for Accurate Eye Diagram Estimation in High Bandwidth Memory (HBM) Silicon Interposer-Reference-Cited by-同舟云学术

Channel Characteristic-Based Deep Neural Network Models for Accurate Eye Diagram Estimation in High Bandwidth Memory (HBM) Silicon Interposer

Published:2021 Issue: Volume: Page:1-13
ISSN:0018-9375
Container-title:IEEE Transactions on Electromagnetic Compatibility
language:
Short-container-title:IEEE Trans. Electromagn. Compat.

Author:

Lho Daehwan,Park Hyunwook,Park Shinyoung,Kim Subin,Kang Hyungmin,Sim Boogyo,Kim Seongguk,Park Junyong,Cho Kyungjun,Song Jinwook,Kim Youngwoo,Kim Joungho

Funder

ANSYS Korea

National Research Foundation of Korea

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Condensed Matter Physics,Atomic and Molecular Physics, and Optics

Link

http://xplorestaging.ieee.org/ielx7/15/4358749/09478064.pdf?arnumber=9478064

Cited by 12 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Predicting the Characteristics of High-Speed Serial Links Based on a Deep Neural Network (DNN)—Transformer Cascaded Model;Electronics;2024-08-02

2. A 3-D Convolutional Network for Fast and Accurate S/Z-Parameters Prediction of Si-Interposer Power Distribution Network With Through Silicon Vias;IEEE Transactions on Circuits and Systems II: Express Briefs;2024-05

3. Policy-Based Reinforcement Learning for Through Silicon Via Array Design in High-Bandwidth Memory Considering Signal Integrity;IEEE Transactions on Electromagnetic Compatibility;2024-02

4. Fast and Accurate Computation of Wireless Power Transfer System Optimal Design Using Particle Swarm Optimization Method;IEEE Transactions on Electromagnetic Compatibility;2023-12

5. An Extended L–2L De-Embedding Method for Modeling and Low Return-Loss Transition of Millimeter Wave Signal Through Silicon Interposer;IEEE Transactions on Electromagnetic Compatibility;2023-12