Application of Neural Network Based Cascade-able Transceiver Model in Serial Link Simulation

Yixuan Zhao, Thong Nguyen, Jose E. Schutt-Aine

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper describes the work-flow of developing black-box transceiver models for channel cascading using feedforward neural network (FNN). Compared to transistor level models, the FNN model is independent of the circuit simulator, whereas the former suffers from slow Newton-Raphson iterations. Furthermore, the generation of FNN model requires no substantial knowledge in circuit design, making it more feasible to the end-users who work in another field.

Original languageEnglish (US)
Title of host publication26th IEEE Workshop on Signal and Power Integrity, SPI 2022 - Conference Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665486255
DOIs
StatePublished - 2022
Event26th IEEE Workshop on Signal and Power Integrity, SPI 2022 - Siegen, Germany
Duration: May 22 2022May 25 2022

Publication series

Name26th IEEE Workshop on Signal and Power Integrity, SPI 2022 - Conference Proceedings

Conference

Conference26th IEEE Workshop on Signal and Power Integrity, SPI 2022
Country/TerritoryGermany
CitySiegen
Period5/22/225/25/22

Keywords

  • cascade
  • channel simulation
  • neural network
  • signal integrity
  • time-domain
  • Transceiver modeling

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Nuclear Energy and Engineering
  • Safety, Risk, Reliability and Quality
  • Control and Optimization
  • Computer Networks and Communications
  • Signal Processing

Fingerprint

Dive into the research topics of 'Application of Neural Network Based Cascade-able Transceiver Model in Serial Link Simulation'. Together they form a unique fingerprint.

Cite this