Abstract

When an electrostatic discharge (ESD) event reaches a nongrounded metallic part within a product, the voltage of this metal with respect to ground will increase. If the isolation to the ground is insufficient, a secondary ESD event can occur. As secondary ESD often leads to system upset or damage, and to poorly reproducible results, it is important to detect the occurrence of secondary ESD. If the discharge current is monitored using an oscilloscope, the test equipment may miss the secondary discharge waveform. This is because the time delay between the primary and secondary discharge events can vary between nanoseconds to milliseconds. Present oscilloscopes do not offer functionality to autodetect a secondary discharge event. The goal of this study is to analyze different types of secondary discharge events acquired with various measurement setups and identify waveform parameters for software-assisted detection methods. A learning sequence is proposed for identifying secondary ESD events starting from low ESD gun test voltages. The data are analyzed with respect to the waveform parameters such as the vertical threshold of the rising edge, the dI/dt of the current waveform, and total charge delivered, which enable automatic detection of secondary ESD while monitoring the discharge waveform at the ESD gun tip.

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