Machine Learning Based Non-Intrusive Inspection Technique to Quantify GaN HEMT Characteristics

High reliability (Hi-Rel) electronics for mission critical applications are handled with extreme care; stress testing upon full assembly can increase a likelihood of degrading these systems before their deployment. Moreover, novel material parts, such as wide bandgap semiconductor devices, tend to have more complicated fabrication processing needs which could ultimately result in larger part variability or potential defects. Therefore, an intelligent screening and inspection technique for electronic parts, in particular gallium nitride (GaN) power transistors, is presented in this paper. We present a machine-learning-based non-intrusive technique that can enhance part-selection decisions to categorize the part samples to the population’s expected electrical characteristics. This technique provides relevant information about GaN HEMT device characteristics without having to operate all of these devices at the high current region of the transfer and output characteristics, lowering the risk of damaging the parts prematurely. The proposed non-intrusive technique uses a small signal pulse width modulation (PWM) of various frequencies, ranging from 10 kHz to 500 kHz, injected into the transistor terminals and the corresponding output signals are observed and used as training dataset. Unsupervised clustering techniques with K-means and feature dimensional reduction through principal component analysis (PCA) have been used to correlate a population of GaN HEMT transistors to the expected mean of the devices’ electrical characteristic performance.