{Reference Type}: Journal Article
{Title}: A Machine Learning Approach to Predict Radiation Effects in Microelectronic Components.
{Author}: Morilla F;Vega J;Dormido-Canto S;Romero-Maestre A;de-Martín-Hernández J;Morilla Y;Martín-Holgado P;Domínguez M;
{Journal}: Sensors (Basel)
{Volume}: 24
{Issue}: 13
{Year}: 2024 Jul 1
{Factor}: 3.847
{DOI}: 10.3390/s24134276
{Abstract}: 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.