Departamento de Física
URI permanente desta comunidadehttps://repositorio.fei.edu.br/handle/FEI/785
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2 resultados
Resultados da Pesquisa
- Modeling of MOSFETs Altered by Ionizing Radiation Using Artificial Neural Networks(2023-08-05) SANTOS, L. S. A. DOS; ALLEGRO, P. R. P.; Marcilei Aparecida Guazzelli; GUIDI, A. L.; G. JUNIOR, P. R.; A. JUNIOR, V. S.; TOUFEN, D. L.; VILAS BOAS, A. C.© 2023, The Author(s) under exclusive licence to Sociedade Brasileira de Física.The ionizing radiation absorbed by semiconductor devices can change their properties by modifying their electrical parameters and, in the case of memories, it can modify the information contained in these components. Thus, the ability to predict how ionizing radiation affects electronic circuits becomes especially important in environments where there is the possibility of prolonged exposure to intense radiation, such as satellites, nuclear reactors, particle accelerators, and medical equipment, among others. In this sense, this paper proposes a methodology to reproduce the behavior of TID (total ionizing dose) damaged MOSFET transistors using the fully connected artificial neural networks, taking advantage of its universal estimator characteristics to oversample the dataset’s pattern and give it a better resolution. The dataset complexity requires a specific architecture choice, being necessary the use of two neural network models to separately reproduce the MOSFET electric current magnitude order and its curve shape. Results show a very good capability to reproduce and interpolate the MOSFET behavior, which makes the proposed method a promising way to simulate circuits based on MOSFETs that are exposed to ionizing radiation.
- 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.