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Title:Automated testing and machine-learning-based modeling of air discharge ESD
Author(s):Sagan, Sam
Advisor(s):Rosenbaum, Elyse
Department / Program:Electrical & Computer Eng
Discipline:Electrical & Computer Engr
Degree Granting Institution:University of Illinois at Urbana-Champaign
Degree:M.S.
Genre:Thesis
Subject(s):Air Discharge
ESD
Electrostatic Discharge
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.
Issue Date:2017-07-20
Type:Thesis
URI:http://hdl.handle.net/2142/98434
Rights Information:Copyright 2017 Sam Sagan
Date Available in IDEALS:2017-09-29
Date Deposited:2017-08


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