Supervised Deep Learning and Classification of Single-Event Transients

Abstract

This article describes a method to detect and classify single-event transients (SET) to determine the originating circuit node impacted by ionizing radiation. SETs were measured via two-photon absorption (TPA) laser excitation on a custom CMOS phase-locked loop (PLL), and Convolutional Neural Networks (CNN) were used to classify the spatial dependencies of the transient responses. A clustering technique is described to identify groups of related circuit nodes and achieves over 90% identification accuracy.