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

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Abstract

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 IB0. A possible cause for this finding was analyzed and confirmed through irradiation experiments.

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Authors and Affiliations

  1. Department of Engineering Physics, Tsinghua University, Bei**g, 100084, China

    Bai-Chuan Wang & Chuan-**ang Tang

  2. Key Laboratory of Particle & Radiation Imaging (Tsinghua University), Ministry of Education, Bei**g, 100084, China

    Bai-Chuan Wang & Chuan-**ang Tang

  3. State Key Laboratory of Intense Pulsed Radiation Simulation and Effect, **’an, 710024, China

    Bai-Chuan Wang, Meng-Tong Qiu, Wei Chen & Chen-Hui Wang

  4. State Key Laboratory of Nuclear Physics and Technology, Peking University, Bei**g, 100871, China

    Chen-Hui Wang

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  1. Bai-Chuan Wang
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  2. Meng-Tong Qiu
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  3. Wei Chen
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Contributions

All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by Bai-Chuan Wang, Meng-Tong Qiu, and Chuan-**ang Tang. The first draft of the manuscript was written by Bai-Chuan Wang and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Chuan-**ang Tang.

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This work was supported by the National Natural Science Foundation of China (Nos. 11690040 and 11690043).

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Wang, BC., Qiu, MT., Chen, W. et al. Machine learning-based analyses for total ionizing dose effects in bipolar junction transistors. NUCL SCI TECH 33, 131 (2022). https://doi.org/10.1007/s41365-022-01107-w

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