Analysis of VF-TLP ESD Measurements Using Machine Learning

In this paper, we propose a machine learning-powered approach to explore analysis of electrostatic discharge (ESD) protection device measurements taken with high-current pulse method. Specifically, the data analyzed was collected under a very fast transmission line pulse (VF - TLP) on a grounded gate NMOS (ggNMOS) protection device. The analysis is done by applying K-means clustering in two complementary steps. First, we use the raw data for a holistic pattern-based clustering to filter out the possible measurement errors. In the second step, the subset that seems error-free is further analyzed through a feature-based clustering that provides insight into parametric variations and stability of the protection design. We also employ visualization techniques, i.e. t-SNE dimensionality reduction and statistical boxplots, to make interpretation of results easier for ESD test and quality engineers. We have validated the proof of concept using measurement data obtained in Kelvin setup for 18 devices.