Engineering Commons^™

Application Of Machine Learning For Predicting Iemi Upset In Multi-Architecture Microcontrollers, Daniel S. Guillette

Electrical and Computer Engineering ETDs

Four microcontrollers were programmed to execute a simple counting program. Pulsed RF signals – also known as Intentional ElectroMagnetic Interference (IEMI) – were injected into the clock input of the microcontrollers. At the same time, the output lines were monitored to determine whether the IEMI signal altered the output of the counting program – referred to as an upset. A state-of-the-art automated testing apparatus was used to collect and process 120,960 samples of IEMI upset data. The data was used to perform a traditional upset trends study and train a series of machine learning (ML) techniques – k-Nearest Neighbors, Support …