Predictive study on a bipolar operational amplifier behaviour under gamma and protons radiation

Romero-Maestre, Amor; Martín-Holgado, Pedro; de-Martín-Hernández, José; Ramírez-García, José Manuel; González-Luján, José J.; Ricca-Soaje, Álvaro; Barbero, Mario Sacristan; Alia, Rubén García; Morilla, Fernando; Domínguez, Manuel; Morilla, Yolanda

doi:10.1109/radecs55911.2022.10412350

2022 22nd European Conference on Radiation and Its Effects on Components and Systems (RADECS) 2022

DOI: 10.1109/radecs55911.2022.10412350

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Predictive study on a bipolar operational amplifier behaviour under gamma and protons radiation

Amor Romero-Maestre,

Pedro Martín-Holgado,

José de-Martín-Hernández

et al.

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2024

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A Machine Learning Approach to Predict Radiation Effects in Microelectronic Components

Morilla,

Vega,

Dormido-Canto

et al. 2024

Sensors

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This paper presents an innovative technique, Advanced Predictor of Electrical Parameters, based on machine learning methods to predict the degradation of electronic components under the effects of radiation. The term degradation refers to the way in which electrical parameters of the electronic components vary with the irradiation dose. This method consists of two sequential steps defined as ‘recognition of degradation patterns in the database’ and ‘degradation prediction of new samples without any kind of irradiation’. The technique can be used under two different approaches called ‘pure data driven’ and ‘model based’. In this paper, the use of Advanced Predictor of Electrical Parameters is shown for bipolar transistors, but the methodology is sufficiently general to be applied to any other component.

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A Machine Learning Approach to Predict Radiation Effects in Microelectronic Components

Morilla,

Vega,

Dormido-Canto

et al. 2024

Sensors

View full text Add to dashboard Cite

show abstract

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Predictive study on a bipolar operational amplifier behaviour under gamma and protons radiation

Cited by 1 publication

References 13 publications

A Machine Learning Approach to Predict Radiation Effects in Microelectronic Components

A Machine Learning Approach to Predict Radiation Effects in Microelectronic Components

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