Automated testing and machine-learning-based modeling of air discharge ESD

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.

Name thesis:degree_name

M.S.

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Electrical & Computer Engr

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2017

Author dc:creator

• Sagan, Sam

Contributors dc:contributor

• Rosenbaum, Elyse

• Air discharge
• Electrostatic discharge (ESD)
• Machine learning

Statement dc:rights

• Copyright 2017 Sam Sagan

Language dc:language

en

Handle dc:identifier

http://hdl.handle.net/2142/98434

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/98434