Time-frequency analysis of electrostatic discharge signal based on wavelet transform

Cong Cheng, Fangming Ruan*, Di Deng, Jia Li, Ming Su, David Pommerenke

*Corresponding author for this work

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

Abstract

Electrostatic discharge signal is a non-stationary signal whose frequency is varied with time. Time-frequency analysis is able to reveal the more useful information hidden in the ESD signal. In this letter, we propose a time-frequency analysis approach using the wavelet transform. Based on the Morlet wavelet, this paper analyzes the actual ESD signal and obtains its time-frequency characteristic. 2-D and 3-D ESD data are showed in this paper. The result shown that high frequency component of ESD can reach 0.6GHz. In addition, the energy of the measured signal is mainly concentrated in the range of 100 to 200 MHz. The high-frequency component attenuations rapidly and the low-frequency duration is relatively long. It can provide some new idea for extraction or signal denoising.

Original languageEnglish
Title of host publicationProceedings of 2018 12th IEEE International Conference on Anti-Counterfeiting, Security, and Identification, ASID 2018
PublisherIEEE Computer Society
Pages35-38
Number of pages4
ISBN (Electronic)9781538660638
DOIs
Publication statusPublished - 2 Jul 2018
Externally publishedYes
Event12th IEEE International Conference on Anti-Counterfeiting, Security, and Identification, ASID 2018 - Xiamen, China
Duration: 9 Nov 201811 Nov 2018

Publication series

NameProceedings of the International Conference on Anti-Counterfeiting, Security and Identification, ASID
Volume2018-November
ISSN (Print)2163-5048
ISSN (Electronic)2163-5056

Conference

Conference12th IEEE International Conference on Anti-Counterfeiting, Security, and Identification, ASID 2018
CountryChina
CityXiamen
Period9/11/1811/11/18

Keywords

  • Electrostatic discharge
  • Time-frequency analysis
  • Wavelet transform

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design
  • Computer Science Applications
  • Computer Vision and Pattern Recognition
  • Software

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  • Cite this

    Cheng, C., Ruan, F., Deng, D., Li, J., Su, M., & Pommerenke, D. (2018). Time-frequency analysis of electrostatic discharge signal based on wavelet transform. In Proceedings of 2018 12th IEEE International Conference on Anti-Counterfeiting, Security, and Identification, ASID 2018 (pp. 35-38). [8681528] (Proceedings of the International Conference on Anti-Counterfeiting, Security and Identification, ASID; Vol. 2018-November). IEEE Computer Society. https://doi.org/10.1109/ICASID.2018.8693107