Self-Organizing Neural Network-Based Analysis of Electrostatic Discharge for Electromagnetic Interference Assessment(Electromagnetic Compatibility(EMC))

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概要

• 論文の詳細を見る

• This paper describes an analysis of the electromagnetic interference (EMI) aspects of electrostatic discharge (ESD), which sometimes causes serious damage to electrical systems. To classify EMI-related properties resulting from ESD events, we used a self-organizing neural network, which can map high-dimensional data into simple geometric relationships on a low-dimensional display. Also, to clarify the effect of a high-speed moving discharge, we generated one-shot discharges repeatedly and measured the ESD current in the time domain to obtain its EMI-related characteristics of this phenomenon. Based on the measured data, we examined several differential properties of ESD wave forms including the maximum amplitude and energy level, and analyzed these multi-dimensional data using the self-organizing neural network scheme. The results showed that the high-speed moving discharges can increase the maximum amplitude, relative energy, and entropy of ESD events, and that the positioning of the EMI level of each ESD event can be effectively visualized in a two-dimensional space.

• 社団法人電子情報通信学会の論文
• 2003-06-01

著者

• Masugi Masao

  Ntt Corp. Musashino‐shi Jpn

• Masugi Masao

  Ntt Corporation

関連論文

• A Two-Dimensional Clustering Approach to the Analysis of Audible Noises Induced at Telephone Terminals(Electromagnetic Compatibility (EMC))
• Self-Organizing Neural Network-Based Analysis of Electrostatic Discharge for Electromagnetic Interference Assessment(Electromagnetic Compatibility(EMC))

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