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
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

Publication series

NameIEEE International Reliability Physics Symposium Proceedings
Volume2018-March
ISSN (Print)1541-7026

Other

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

Keywords

  • System-level ESD
  • air discharge
  • stochastic modeling

ASJC Scopus subject areas

  • Engineering(all)

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    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 (pp. 2C.21-2C.210). (IEEE International Reliability Physics Symposium Proceedings; Vol. 2018-March). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IRPS.2018.8353548