@inproceedings{e8f747c9bdbf4579838e43296bf9c8dd,
title = "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.",
keywords = "component, Discharge Parameters, Electrode Moving Speed, Electrostatic Discharge, Gas Pressure, Neural Network",
author = "Huang Jun and Ruan Fangming and Su Ming and Wang Heng and Yang Xiangdong and David Pommerenke",
year = "2018",
month = jan,
day = "16",
doi = "10.1109/EMC-B.2017.8260479",
language = "English",
series = "IEEE International Symposium on Electromagnetic Compatibility",
publisher = "Institute of Electrical and Electronics Engineers",
pages = "1--6",
booktitle = "Proceedings of 2017 IEEE 5th International Symposium on Electromagnetic Compatibility, EMC-Beijing 2017",
address = "United States",
note = "5th IEEE International Symposium on Electromagnetic Compatibility, EMC-Beijing 2017 ; Conference date: 28-10-2017 Through 31-10-2017",
}