To clarify the characteristics of high-speed electrostatic discharge (ESD) events, we use two kinds of discharge electrodes: sphere- and cylinder-shape ones. We measure the energy level of ESD waveforms with charging voltages of 0.25, 0.5, and 1.0 kV. We find that the cylindrical electrode yields higher high-speed ESD energies, especially when the charging voltage is high; this indicates that the discharge gap shape is an important factor in ESD events.

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 waveforms 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.