Stochastic modeling of air electrostatic discharge parameters

Yang Xiu, Samuel Sagan, Advika Battini, Xiao Ma, Maxim Raginsky, Elyse Rosenbaum

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

Abstract

An automated tester is built for IEC 61000-4-2 air discharges. The relations between the parameters of the resulting waveforms are studied using stochastic modeling. The precharge voltage, peak current and rise time are interrelated, with a strong dependence on the humidity. However, there is no clear dependence of the peak current and rise time on the approach speed. Naïve Bayes method is used to predict the peak current and rise time from the precharge voltage and humidity. The likelihood that a tablet experiences a soft failure is predicted via logistic regression.

Original languageEnglish (US)
Title of host publication2018 IEEE International Reliability Physics Symposium, IRPS 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2C.21-2C.210
Volume2018-March
ISBN (Electronic)9781538654798
DOIs
StatePublished - May 25 2018
Event2018 IEEE International Reliability Physics Symposium, IRPS 2018 - Burlingame, United States
Duration: Mar 11 2018Mar 15 2018

Other

Other2018 IEEE International Reliability Physics Symposium, IRPS 2018
CountryUnited States
CityBurlingame
Period3/11/183/15/18

Fingerprint

Electrostatic discharge
Atmospheric humidity
Electric potential
Air
Discharge (fluid mechanics)
Logistics

Keywords

  • air discharge
  • stochastic modeling
  • System-level ESD

ASJC Scopus subject areas

  • Engineering(all)

Cite this

Xiu, Y., Sagan, S., Battini, A., Ma, X., Raginsky, M., & Rosenbaum, E. (2018). Stochastic modeling of air electrostatic discharge parameters. In 2018 IEEE International Reliability Physics Symposium, IRPS 2018 (Vol. 2018-March, pp. 2C.21-2C.210). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IRPS.2018.8353548

Stochastic modeling of air electrostatic discharge parameters. / Xiu, Yang; Sagan, Samuel; Battini, Advika; Ma, Xiao; Raginsky, Maxim; Rosenbaum, Elyse.

2018 IEEE International Reliability Physics Symposium, IRPS 2018. Vol. 2018-March Institute of Electrical and Electronics Engineers Inc., 2018. p. 2C.21-2C.210.

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

Xiu, Y, Sagan, S, Battini, A, Ma, X, Raginsky, M & Rosenbaum, E 2018, Stochastic modeling of air electrostatic discharge parameters. in 2018 IEEE International Reliability Physics Symposium, IRPS 2018. vol. 2018-March, Institute of Electrical and Electronics Engineers Inc., pp. 2C.21-2C.210, 2018 IEEE International Reliability Physics Symposium, IRPS 2018, Burlingame, United States, 3/11/18. https://doi.org/10.1109/IRPS.2018.8353548
Xiu Y, Sagan S, Battini A, Ma X, Raginsky M, Rosenbaum E. Stochastic modeling of air electrostatic discharge parameters. In 2018 IEEE International Reliability Physics Symposium, IRPS 2018. Vol. 2018-March. Institute of Electrical and Electronics Engineers Inc. 2018. p. 2C.21-2C.210 https://doi.org/10.1109/IRPS.2018.8353548
Xiu, Yang ; Sagan, Samuel ; Battini, Advika ; Ma, Xiao ; Raginsky, Maxim ; Rosenbaum, Elyse. / Stochastic modeling of air electrostatic discharge parameters. 2018 IEEE International Reliability Physics Symposium, IRPS 2018. Vol. 2018-March Institute of Electrical and Electronics Engineers Inc., 2018. pp. 2C.21-2C.210
@inproceedings{76890f8e7f6f4a118976eb8a5ef1d33d,
title = "Stochastic modeling of air electrostatic discharge parameters",
abstract = "An automated tester is built for IEC 61000-4-2 air discharges. The relations between the parameters of the resulting waveforms are studied using stochastic modeling. The precharge voltage, peak current and rise time are interrelated, with a strong dependence on the humidity. However, there is no clear dependence of the peak current and rise time on the approach speed. Na{\"i}ve Bayes method is used to predict the peak current and rise time from the precharge voltage and humidity. The likelihood that a tablet experiences a soft failure is predicted via logistic regression.",
keywords = "air discharge, stochastic modeling, System-level ESD",
author = "Yang Xiu and Samuel Sagan and Advika Battini and Xiao Ma and Maxim Raginsky and Elyse Rosenbaum",
year = "2018",
month = "5",
day = "25",
doi = "10.1109/IRPS.2018.8353548",
language = "English (US)",
volume = "2018-March",
pages = "2C.21--2C.210",
booktitle = "2018 IEEE International Reliability Physics Symposium, IRPS 2018",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
address = "United States",

}

TY - GEN

T1 - Stochastic modeling of air electrostatic discharge parameters

AU - Xiu, Yang

AU - Sagan, Samuel

AU - Battini, Advika

AU - Ma, Xiao

AU - Raginsky, Maxim

AU - Rosenbaum, Elyse

PY - 2018/5/25

Y1 - 2018/5/25

N2 - An automated tester is built for IEC 61000-4-2 air discharges. The relations between the parameters of the resulting waveforms are studied using stochastic modeling. The precharge voltage, peak current and rise time are interrelated, with a strong dependence on the humidity. However, there is no clear dependence of the peak current and rise time on the approach speed. Naïve Bayes method is used to predict the peak current and rise time from the precharge voltage and humidity. The likelihood that a tablet experiences a soft failure is predicted via logistic regression.

AB - An automated tester is built for IEC 61000-4-2 air discharges. The relations between the parameters of the resulting waveforms are studied using stochastic modeling. The precharge voltage, peak current and rise time are interrelated, with a strong dependence on the humidity. However, there is no clear dependence of the peak current and rise time on the approach speed. Naïve Bayes method is used to predict the peak current and rise time from the precharge voltage and humidity. The likelihood that a tablet experiences a soft failure is predicted via logistic regression.

KW - air discharge

KW - stochastic modeling

KW - System-level ESD

UR - http://www.scopus.com/inward/record.url?scp=85046947552&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85046947552&partnerID=8YFLogxK

U2 - 10.1109/IRPS.2018.8353548

DO - 10.1109/IRPS.2018.8353548

M3 - Conference contribution

AN - SCOPUS:85046947552

VL - 2018-March

SP - 2C.21-2C.210

BT - 2018 IEEE International Reliability Physics Symposium, IRPS 2018

PB - Institute of Electrical and Electronics Engineers Inc.

ER -