A Machine Learning Approach to Predicting Single Event Upsets

• URL: http://arxiv.org/abs/2310.05878v1
• Date: Mon, 9 Oct 2023 17:19:49 GMT
• Title: A Machine Learning Approach to Predicting Single Event Upsets
• Authors: Archit Gupta, Chong Yock Eng, Deon Lim Meng Wee, Rashna Analia Ahmed, See Min Sim
• Abstract summary: A single event upset (SEU) is a critical soft error that occurs in semiconductor devices on exposure to ionising particles from space environments. Currently, SEUs are only detected several hours after their occurrence. CREMER, the model presented in this paper, predicts SEUs in advance using machine learning.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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