A Machine Learning Approach to Predicting Single Event Upsets
This addresses safety hazards in space vehicles by enabling early detection of SEUs, though it appears incremental as it builds on existing prediction methods with a focus on robustness and scalability.
The paper tackles the problem of predicting single event upets (SEUs) in semiconductor devices, which cause bit flips and safety hazards in space environments, by introducing CREMER, a machine learning model that uses only positional data to predict SEUs in advance, achieving improved reliability for memory devices.
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.