Automated testing and machine-learning-based modeling of air discharge ESD
Sagan, Sam
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https://hdl.handle.net/2142/98434
Description
Title
Automated testing and machine-learning-based modeling of air discharge ESD
Author(s)
Sagan, Sam
Issue Date
2017-07-20
Director of Research (if dissertation) or Advisor (if thesis)
Rosenbaum, Elyse
Department of Study
Electrical & Computer Eng
Discipline
Electrical & Computer Engr
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Keyword(s)
Air discharge
Electrostatic discharge (ESD)
Machine learning
Abstract
An IEC 16000-4-2 compliant, high-accuracy air-discharge automation system is used to study the properties of air discharge electrostatic discharge (ESD). This work corroborates conclusions of previous works and presents new insights into the effects of approach speed on ESD. A methodology for machine-learning-based ESD modeling is presented. Models are validated with a high degree of accuracy against measurement data.
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