As the number and complexity of electronic parts increases with every vehicle, the demands for reliability continually increase. This is even more acute when deploying advanced materials such as SiC and GaN to address the ever more stringent demands for electric powertrains.
An advanced statistical screening approach is proposed for the purpose of identification of electronic parts that have an elevated risk of field failure. A comparison is made between the proposed Omnivariate approach, industry-standard DPAT, and more commonly recognized multivariate methods such as Mahalanobis Distance and Hoteling T^2, which tend to provide less useful information as the number of monitored parameters exceeds a few hundred.
A few examples from Silicon CMOS and SiC Power device manufacturing are used for demonstration.