Useinov et al., Simulation of bipolar transistor degradation at various dose rates and electrical modes for high dose conditions. Fleetwood, Total ionizing dose effects in MOS and low-dose-rate-sensitive linear-bipolar devices.

Fleetwood, ELDRS in bipolar linear circuits: a review. Fleetwood et al., Hydrogen soaking, displacement damage effects, and charge yield in gated lateral bipolar junction transistors. Chen et al., Synergistic effects of TID and ATREE in vertical NPN bipolar transistor. A possible cause for this finding was analyzed and confirmed through irradiation experiments. The trained ANN model suggests that the TID hardness of a BJT tends to increase with base current I B0. The results indicate that the proposed ANN model has advantages in capturing nonlinear correlations and predicting the data.

An ANN model was built and trained using data collected from different experiments. In this study, we proposed a new artificial neural network (ANN) approach to analyze the experimental data of TID effects on BJTs. However, it is difficult to utilize these data effectively. Currently, many experiments of TID effects on BJTs have been conducted at different laboratories worldwide, producing a large amount of experimental data, which provides a wealth of information. The total ionizing dose (TID) effects usually cause gain degradation of bipolar junction transistors (BJTs), leading to functional failures of bipolar integrated circuits. In this paper, we show that research on radiation effects could benefit from such methods and present a machine learning-based scientific discovery approach. Machine learning methods have proven to be powerful in various research fields.

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