Single-Event Burnout in Semiconductor Devices: Efficient Classification of Ion Species

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.