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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.

GaN high-electron-mobility transistors (HEMTs) are promising next-generation devices in the power electronics field which can coexist with silicon semiconductors, mainly in some radiation-intensive environments, such as power space converters, where high frequencies and voltages are also needed. Its wide band gap (WBG), large breakdown electric field, and thermal stability improve actual silicon performances. However, at the moment, GaN HEMT technology suffers from some reliability issues, one of the more relevant of which is the dynamic on-state resistance (R ON_dyn ) regarding power switching converter applications. In this study, we focused on the drain-to-source on-resistance (R DSON ) characteristics under 60 Co gamma radiation of two different commercial power GaN HEMT structures. Different bias conditions were applied to both structures during irradiation and some static measurements, such as threshold voltage and leakage currents, were performed. Additionally, dynamic resistance was measured to obtain practical information about device trapping under radiation during switching mode, and how trapping in the device is affected by gamma radiation. The experimental results showed a high dependence on the HEMT structure and the bias condition applied during irradiation. Specifically, a free current collapse structure showed great stability until 3.7 Mrad(Si), unlike the other structure tested, which showed high degradation of the parameters measured. The changes were demonstrated to be due to trapping effects generated or enhanced by gamma radiation. These new results obtained about R ON_dyn will help elucidate trap behaviors in switching transistors.