Enabling Online GaN Power Device Self-Health Monitoring With Analog SGD Supervis

Abstract

In order to mitigate reliability challenges on emerging wide-bandgap (WBG) gallium nitride (GaN) power devices, this article investigates a software–hardware codesign solution utilizing online device condition monitoring and machine-learning technologies, with an ultimate goal of achieving intelligent device self-health learning efficiently and effectively. Specifically, an on-die logarithm-based analog stochastic gradient descent (SGD) supervised learning engine is developed to train a GaN powe