Machine learning-based analyses for total ionizing dose effects in bipolar junction transistors

Machine learning methods have proven to be powerful in various research fields. In this paper, we show that research on radiation effects could benefit from such methods and present a machine learning-based scientific discovery approach. The total ionizing dose (TID) effects usually cause gain degra...

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Bibliographic Details
Published in:Nuclear science and techniques 2022-10, Vol.33 (10), p.106-116, Article 131
Main Authors: Wang, Bai-Chuan, Qiu, Meng-Tong, Chen, Wei, Wang, Chen-Hui, Tang, Chuan-Xiang
Format: Article
Language:English
Subjects:
Beam Physics
Nuclear Energy
Particle Acceleration and Detection
Particle and Nuclear Physics
Physics
Physics and Astronomy
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Summary:Machine learning methods have proven to be powerful in various research fields. In this paper, we show that research on radiation effects could benefit from such methods and present a machine learning-based scientific discovery approach. The total ionizing dose (TID) effects usually cause gain degradation of bipolar junction transistors (BJTs), leading to functional failures of bipolar integrated circuits. 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. However, it is difficult to utilize these data effectively. In this study, we proposed a new artificial neural network (ANN) approach to analyze the experimental data of TID effects on BJTs. An ANN model was built and trained using data collected from different experiments. The results indicate that the proposed ANN model has advantages in capturing nonlinear correlations and predicting the data. The trained ANN model suggests that the TID hardness of a BJT tends to increase with base current I B0 . A possible cause for this finding was analyzed and confirmed through irradiation experiments.
ISSN:1001-8042
2210-3147
DOI:10.1007/s41365-022-01107-w