Repositório do Conhecimento Institucional do Centro Universitário FEI
 

Departamento de Física

URI permanente desta comunidadehttps://repositorio.fei.edu.br/handle/FEI/785

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Filtros

2022 - 20232

Configurações

Autor: search.filters.author.AGUIAR, V. A. P.Periódico: search.filters.ispartof.Microelectronics Reliability

Resultados da Pesquisa

Agora exibindo 1 - 2 de 2
  • N/D
    Artigo 7 Citação(ões) na Scopus
    Unsupervised machine learning application to identify single-event transients (SETs) from noise events in MOSFET transistor ionizing radiation effects
    (2023) 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 Guazzelli; MELO, M. A. A.; OLIVEIRA, J. A.
    © 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.
  • N/D
    Artigo 4 Citação(ões) na Scopus
    Charge deposition analysis of heavy-ion-induced single-event burnout in low-voltage power VDMOSFET
    (2022-10-05) ALBERTON, S. G.; AGUIAR, V. A. P.; MEDINA, N. H.; ADDED, N.; MACCHIONE, E. L. A.; MENEGASSO, R.; CESARIO. G. J.; SANTOS, H. C.; SCARDUELLI, V. B.; ALCANTARA-NUNEZ, J. A.; Marcilei Aparecida Guazzelli; Roberto Santos; FLECHAS, D.
    © 2022 Elsevier LtdThe heavy-ion-induced single-event burnout (SEB) risk in power MOSFETs (metal-oxide-semiconductor field-effect transistors) can be assessed in ground facilities, although it is costly and time-demanding. For this reason, there have been few experimental studies dedicated to investigate the relevant parameter related to the description of ion-induced SEB phenomenon. In this work the heavy-ion-induced SEB in a low-voltage power VDMOSFET (vertical double-diffused MOSFET) is studied using several ion-energy combinations. A self-consistent statistical analysis is carried out in order to elucidate the relationship between charge deposition and SEB triggering. Experimental data is compared to a predictive model from the literature for SEE (single-event effect) worst-case prediction in power MOSFETs, supporting for the first time its relevance to the worst-case prediction in the SEB mechanism.