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The emergence of new nanoscale technologies has imposed significantchallenges to designing reliable electronic systems in radiation environments.A few types of radiation like Total Ionizing Dose (TID) effects often causepermanent damages on such nanoscale electronic devices, and currentstate-of-the-art technologies to tackle TID make use of expensiveradiation-hardened devices. This paper focuses on a novel and differentapproach: using machine learning algorithms on consumer electronic level FieldProgrammable Gate Arrays (FPGAs) to tackle TID effects and monitor them toreplace before they stop working. This condition has a research challenge toanticipate when the board results in a total failure due to TID effects. Weobserved internal measurements of the FPGA boards under gamma radiation andused three different anomaly detection machine learning (ML) algorithms todetect anomalies in the sensor measurements in a gamma-radiated environment.The statistical results show a highly significant relationship between thegamma radiation exposure levels and the board measurements. Moreover, ouranomaly detection results have shown that a One-Class Support Vector Machinewith Radial Basis Function Kernel has an average Recall score of 0.95. Also,all anomalies can be detected before the boards stop working.

Using Machine Learning for Anomaly Detection on a System-on-Chip under Gamma Radiation