ALLEGRO, P. R. P.TOUFEN, D. L.AGUIAR, V. A. P.SANTOS, L. S. A.OLIVEIRA W. N.ADDED, N.MEDINA, N. H.MACCHIONE, E. L. A.ALBERTON , S. G.Marcilei Aparecida GuazzelliMELO, M. A. A.OLIVEIRA, J. A.2023-03-272023-03-272023ALLEGRO, P. R. P.; TOUFEN, D. L; AGUIAR, V. A. P.; SANTOS, L. S. A.; OLIVEIRA, W. N.; ADDED, N.; MEDINA, N. H.; MACCHIONE, E. L. A.; ALBERTON, S. G.; GUAZZELLI, M. A.; MELO , M. A. A.; OLIVEIRA, J. A. Unsupervised machine learning application to identify single-event transients (SETs) from noise events in MOSFET transistor ionizing radiation effects. Microelectronics Reliability, v. 142, march, 2023.0026-2714https://repositorio.fei.edu.br/handle/FEI/4755© 2023This article presents a novel application of the k-means unsupervised machine learning algorithm to the problem of identifying single event transient (SET) events from noise during heavy-ion irradiation experiments of an electronic device. We explore the performance of the k-means algorithm by analyzing experimental datasets of SET events produced by several heavy-ions irradiations of a MOSFET transistor. Data anomalies and effectiveness of the chosen features (mean, standard deviation, skewness, and kurtosis) were investigated using the Isolation Forest and Random Forest algorithms, respectively. The results show a high capability of the K-means algorithm to identify SET events from noise using the first four statistical moments as features, allowing in the future the use of this method for in situ event detection and diagnosis without previous algorithm training or pre-analysis of the experimental data.Acesso RestritoArtigo10.1016/j.microrel.2023.114916Isolation Forest anomaly detectionK-means clusteringMachine learningMOSFETRadiation effectsRandom Forest