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1
Machine Learning Approaches for Analysis of Total Ionizing ..:
, In:
2022 22nd European Conference on Radiation and Its Effects on Components and Systems (RADECS)
,
Dean, B.
;
Peyton, T.
;
Carpenter, J. L.
... - p. 1-7 , 2022
Link:
https://doi.org/10.1109/RADECS55911.2022.10412523
RT T1 <cTitle>2022 22nd European Conference on Radiation and Its Effects on Components and Systems (RADECS)</cTitle>: T1 <Title>Machine Learning Approaches for Analysis of Total Ionizing Dose in Microelectronics</Title> UL https://suche.suub.uni-bremen.de/peid=ieee-10412523&Exemplar=1&LAN=DE A1 Dean, B. A1 Peyton, T. A1 Carpenter, J. L. A1 Sam, D. A1 Peterson, A. A1 Kim, J. A1 Lawrence, S. P. A1 Fadul, M. A1 Reising, D. R. A1 Loveless, T. D. YR 2022 SN 1609-0438 K1 Analytical models K1 X-rays K1 Predictive models K1 Real-time systems K1 Convolutional neural networks K1 Total ionizing dose K1 Clocks K1 Convolutional Neural Network K1 Machine Learning K1 Radiation Effects K1 Total Ionizing Dose K1 Regression K1 Classification K1 Complex Digital Device SP 1 OP 7 LK http://dx.doi.org/https://doi.org/10.1109/RADECS55911.2022.10412523 DO https://doi.org/10.1109/RADECS55911.2022.10412523 SF ELIB - SuUB Bremen
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