A Machine Learning Approach to Predicting Single Event Upsets
Archit Gupta Chong Yock Eng Deon Lim Meng Wee Rashna Analia Ahmed See Min Sim
A single event upset (SEU) is a critical soft error that occurs in
semiconductor devices on exposure to ionising particles from space
environments. SEUs cause bit flips in the memory component of semiconductors.
This creates a multitude of safety hazards as stored information becomes less
reliable. Currently, SEUs are only detected several hours after their
occurrence. CREMER, the model presented in this paper, predicts SEUs in advance
using machine learning. CREMER uses only positional data to predict SEU
occurrence, making it robust, inexpensive and scalable. Upon implementation,
the improved reliability of memory devices will create a digitally safer
environment onboard space vehicles.