Eye-Width and Eye-Height Estimation Method Based on Artificial Neural Network (ANN) for USB 3.0

Daehwan Lho; Junyong Park; Hyunwook Park; Hyungmin Kang; Shinyoung Park; Joungho Kim

doi:10.1109/EPEPS.2018.8534277

Eye-Width and Eye-Height Estimation Method Based on Artificial Neural Network (ANN) for USB 3.0

Daehwan Lho
, Junyong Park
, Hyunwook Park
, Hyungmin Kang
, Shinyoung Park
, Joungho Kim

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

17 Citations (Scopus)

Abstract

As technology develops, the amount of data used increases exponentially and the operating speed of electronic devices increases accordingly. As the operating speed increases, the transmission speed of data exchanged through the channel also increases. To get an eye-diagram, a time-domain simulation is required, which consumes a lot of time and computer power. However, a channel simulation in frequency-domain is fast and it consumes less computer power. Therefore, using the frequency-domain simulation data is more efficient than the time-domain simulation. This paper proposes an eye-width and eye-height estimation method using an artificial neural network (ANN). The input of the ANN is insertion loss and the outputs of the ANN are eye-width and eye-height. Finally, the performance of the proposed method is verified by transient eye-diagram simulations with arbitrarily-selected channel parameters.

Original language	English
Title of host publication	EPEPS 2018 - IEEE 27th Conference on Electrical Performance of Electronic Packaging and Systems
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	209-211
Number of pages	3
ISBN (Electronic)	9781538693032
DOIs	https://doi.org/10.1109/EPEPS.2018.8534277
Publication status	Published - 13 Nov 2018
Event	27th IEEE Conference on Electrical Performance of Electronic Packaging and Systems, EPEPS 2018 - San Jose, United States Duration: 14 Oct 2018 → 17 Oct 2018

Publication series

Name	EPEPS 2018 - IEEE 27th Conference on Electrical Performance of Electronic Packaging and Systems

Conference

Conference	27th IEEE Conference on Electrical Performance of Electronic Packaging and Systems, EPEPS 2018
Country/Territory	United States
City	San Jose
Period	14/10/18 → 17/10/18

Bibliographical note

Publisher Copyright:
© 2018 IEEE.

Keywords

Artificial neural network (ANN)
Eye-diagram
Eye-diagram Estimation method
Signal integrity (SI)

Access to Document

10.1109/EPEPS.2018.8534277

Cite this

Lho, D., Park, J., Park, H., Kang, H., Park, S., & Kim, J. (2018). Eye-Width and Eye-Height Estimation Method Based on Artificial Neural Network (ANN) for USB 3.0. In EPEPS 2018 - IEEE 27th Conference on Electrical Performance of Electronic Packaging and Systems (pp. 209-211). Article 8534277 (EPEPS 2018 - IEEE 27th Conference on Electrical Performance of Electronic Packaging and Systems). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/EPEPS.2018.8534277

Lho, Daehwan ; Park, Junyong ; Park, Hyunwook et al. / Eye-Width and Eye-Height Estimation Method Based on Artificial Neural Network (ANN) for USB 3.0. EPEPS 2018 - IEEE 27th Conference on Electrical Performance of Electronic Packaging and Systems. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 209-211 (EPEPS 2018 - IEEE 27th Conference on Electrical Performance of Electronic Packaging and Systems).

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title = "Eye-Width and Eye-Height Estimation Method Based on Artificial Neural Network (ANN) for USB 3.0",

abstract = "As technology develops, the amount of data used increases exponentially and the operating speed of electronic devices increases accordingly. As the operating speed increases, the transmission speed of data exchanged through the channel also increases. To get an eye-diagram, a time-domain simulation is required, which consumes a lot of time and computer power. However, a channel simulation in frequency-domain is fast and it consumes less computer power. Therefore, using the frequency-domain simulation data is more efficient than the time-domain simulation. This paper proposes an eye-width and eye-height estimation method using an artificial neural network (ANN). The input of the ANN is insertion loss and the outputs of the ANN are eye-width and eye-height. Finally, the performance of the proposed method is verified by transient eye-diagram simulations with arbitrarily-selected channel parameters.",

keywords = "Artificial neural network (ANN), Eye-diagram, Eye-diagram Estimation method, Signal integrity (SI)",

author = "Daehwan Lho and Junyong Park and Hyunwook Park and Hyungmin Kang and Shinyoung Park and Joungho Kim",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 27th IEEE Conference on Electrical Performance of Electronic Packaging and Systems, EPEPS 2018 ; Conference date: 14-10-2018 Through 17-10-2018",

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doi = "10.1109/EPEPS.2018.8534277",

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Lho, D, Park, J, Park, H, Kang, H, Park, S & Kim, J 2018, Eye-Width and Eye-Height Estimation Method Based on Artificial Neural Network (ANN) for USB 3.0. in EPEPS 2018 - IEEE 27th Conference on Electrical Performance of Electronic Packaging and Systems., 8534277, EPEPS 2018 - IEEE 27th Conference on Electrical Performance of Electronic Packaging and Systems, Institute of Electrical and Electronics Engineers Inc., pp. 209-211, 27th IEEE Conference on Electrical Performance of Electronic Packaging and Systems, EPEPS 2018, San Jose, United States, 14/10/18. https://doi.org/10.1109/EPEPS.2018.8534277

Eye-Width and Eye-Height Estimation Method Based on Artificial Neural Network (ANN) for USB 3.0. / Lho, Daehwan; Park, Junyong; Park, Hyunwook et al.
EPEPS 2018 - IEEE 27th Conference on Electrical Performance of Electronic Packaging and Systems. Institute of Electrical and Electronics Engineers Inc., 2018. p. 209-211 8534277 (EPEPS 2018 - IEEE 27th Conference on Electrical Performance of Electronic Packaging and Systems).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Lho, Daehwan

AU - Park, Junyong

AU - Park, Hyunwook

AU - Kang, Hyungmin

AU - Park, Shinyoung

AU - Kim, Joungho

PY - 2018/11/13

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AB - As technology develops, the amount of data used increases exponentially and the operating speed of electronic devices increases accordingly. As the operating speed increases, the transmission speed of data exchanged through the channel also increases. To get an eye-diagram, a time-domain simulation is required, which consumes a lot of time and computer power. However, a channel simulation in frequency-domain is fast and it consumes less computer power. Therefore, using the frequency-domain simulation data is more efficient than the time-domain simulation. This paper proposes an eye-width and eye-height estimation method using an artificial neural network (ANN). The input of the ANN is insertion loss and the outputs of the ANN are eye-width and eye-height. Finally, the performance of the proposed method is verified by transient eye-diagram simulations with arbitrarily-selected channel parameters.

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Lho D, Park J, Park H, Kang H, Park S, Kim J. Eye-Width and Eye-Height Estimation Method Based on Artificial Neural Network (ANN) for USB 3.0. In EPEPS 2018 - IEEE 27th Conference on Electrical Performance of Electronic Packaging and Systems. Institute of Electrical and Electronics Engineers Inc. 2018. p. 209-211. 8534277. (EPEPS 2018 - IEEE 27th Conference on Electrical Performance of Electronic Packaging and Systems). doi: 10.1109/EPEPS.2018.8534277

Eye-Width and Eye-Height Estimation Method Based on Artificial Neural Network (ANN) for USB 3.0

Abstract

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Bibliographical note

Keywords

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