Effect Analysis of Factors based on Neural Network in Non-Contact Electrostatic Discharge
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.
Recommended Citation
H. Jun et al., "Effect Analysis of Factors based on Neural Network in Non-Contact Electrostatic Discharge," Proceedings of the 2017 IEEE 5th International Symposium on Electromagnetic Compatibility (2017, Beijing, China), Institute of Electrical and Electronics Engineers (IEEE), Oct 2017.
The definitive version is available at https://doi.org/10.1109/EMC-B.2017.8260479
Meeting Name
2017 IEEE 5th International Symposium on Electromagnetic Compatibility, EMC-Beijing 2017 (2017: Oct. 28-31, Beijing, China)
Department(s)
Electrical and Computer Engineering
Keywords and Phrases
Electrodes; Electromagnetic compatibility; Electrostatic devices; Electrostatic discharge; Neural networks; Component; Discharge current waveforms; Discharge parameters; Electro-Static Discharge (ESD); Gas pressures; International standards; Moving speed; Temperature and humidities; Electric discharges; Electrode Moving Speed
International Standard Book Number (ISBN)
978-1-5090-5185-4
International Standard Serial Number (ISSN)
1077-4076
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2017 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.
Publication Date
01 Oct 2017
Comments
This work was supported by National Nature Science Foundation of China (No. 60971078); by 2016 Electrostatic Research Fund of Academician Liu Sanghe of Beijing Oriental Institute of Metrology and Test(No.BOIMTLSHJD20161004); by 2016 Guizhou Province Electrostatic and Electromagnetic Protection Innovation Talents Team of Sci. and Tech.; by 2016 Central Government Special Fund Supporting Guizhou Development of Science and Technology(No. QKZYD[2016]4006); Guizhou Province Innovation Talents Team of Electrostatic and Electromagnetic Protection(No.QKHPTRC[2017]5653 ).