Year: 2024
Type: article
Abstract: Single-event burnout (SEB) represents a significant challenge to the reliability of semiconductor devices, especially, but not only, in radiation-rich environments such as space and nuclear facilities. This study presents a novel method for classifying SEB events in power semiconductor devices, leveraging ion species identification through Deep Neural Networks (DNN) and Transfer Learning (TL). Adapting a pre-trained DNN originally developed for non-destructive radiation effects enables accurate SEB classification in a different transistor model. This approach is highly effective for designing more resilient power devices, minimizing the need for large datasets and reducing processing time by 39%. With an accuracy of 88%, the method offers a faster and cost-effective solution for gathering insights into the underlying physics, requiring fewer devices and fewer computational resources.
Authors: Julia T. Santos, Reinaldo A. C. Bianchi, Marcilei A. Guazzelli, Juliano A. De Oliveira, Saulo G. Alberton, Renato C. Giacomini
Institutions: Centro Universitário FEI, Universidade de São Paulo
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4406219906