Effect analysis of factors based on neural network in non-contact electrostatic discharge

Huang Jun, Ruan Fangming*, Su Ming, Wang Heng, Yang Xiangdong, David Pommerenke

*Corresponding author for this work

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

Abstract

Discharge parameters in non-contact electrostatic discharge(ESD) are affected by various factors, including electrode moving speed to the target, gas pressure, temperature, humidity. Mechanism of non-contact electrostatic discharge was analyzed based on a neural network model and compared to current waveform in non-contact electrostatic discharge measured with new measurement system of electrostatic discharge. Neural network method was used to adjust the weight of discharge parameters in non-contact electrostatic discharge based on discharge current waveform affected by electrode moving speed, gas pressure, temperature and humidity, so as to compare with the experiment results waveforms met to the requirement of international standard IEC61000-4-2, and to analyze the main parameters that affect discharge currents in non-contact electrostatic discharge events.

Original languageEnglish
Title of host publicationProceedings of 2017 IEEE 5th International Symposium on Electromagnetic Compatibility, EMC-Beijing 2017
PublisherInstitute of Electrical and Electronics Engineers
Pages1-6
Number of pages6
ISBN (Electronic)9781509051854
DOIs
Publication statusPublished - 16 Jan 2018
Externally publishedYes
Event5th IEEE International Symposium on Electromagnetic Compatibility, EMC-Beijing 2017 - Beijing, China
Duration: 28 Oct 201731 Oct 2017

Publication series

NameIEEE International Symposium on Electromagnetic Compatibility
Volume2017-October
ISSN (Print)1077-4076
ISSN (Electronic)2158-1118

Conference

Conference5th IEEE International Symposium on Electromagnetic Compatibility, EMC-Beijing 2017
CountryChina
CityBeijing
Period28/10/1731/10/17

Keywords

  • component
  • Discharge Parameters
  • Electrode Moving Speed
  • Electrostatic Discharge
  • Gas Pressure
  • Neural Network

ASJC Scopus subject areas

  • Condensed Matter Physics
  • Electrical and Electronic Engineering

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  • Cite this

    Jun, H., Fangming, R., Ming, S., Heng, W., Xiangdong, Y., & Pommerenke, D. (2018). Effect analysis of factors based on neural network in non-contact electrostatic discharge. In Proceedings of 2017 IEEE 5th International Symposium on Electromagnetic Compatibility, EMC-Beijing 2017 (pp. 1-6). (IEEE International Symposium on Electromagnetic Compatibility; Vol. 2017-October). Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/EMC-B.2017.8260479