Effect Analysis of Factors based on Neural Network in Non-Contact Electrostatic Discharge
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.
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
2017 IEEE 5th International Symposium on Electromagnetic Compatibility, EMC-Beijing 2017 (2017: Oct. 28-31, Beijing, China)
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)
International Standard Serial Number (ISSN)
Article - Conference proceedings
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01 Oct 2017