典型文献
Machine learning-based analyses for total ionizing dose effects in bipolar junction transistors
文献摘要：
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 transis-tors(BJTs),leading to functional failures of bipolar inte-grated 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 non-linear correlations and predicting the data.The trained ANN model suggests that the TID hardness of a BJT tends to increase with base current IBo.A possible cause for this finding was analyzed and confirmed through irradiation experiments.
文献关键词：
中图分类号：
作者姓名：
Bai-Chuan Wang;Meng-Tong Qiu;Wei Chen;Chen-Hui Wang;Chuan-Xiang Tang
作者机构：
Department of Engineering Physics,Tsinghua University,Beijing 100084,China;Key Laboratory of Particle&Radiation Imaging(Tsinghua University),Ministry of Education,Beijing 100084,China;State Key Laboratory of Intense Pulsed Radiation Simulation and Effect,Xi'an 710024,China;State Key Laboratory of Nuclear Physics and Technology,Peking University,Beijing 100871,China
文献出处：
引用格式：
[1]Bai-Chuan Wang;Meng-Tong Qiu;Wei Chen;Chen-Hui Wang;Chuan-Xiang Tang-.Machine learning-based analyses for total ionizing dose effects in bipolar junction transistors)[J].核技术（英文版）,2022(10):106-116
A类：
BJTs,IBo
B类：
Machine,learning,analyses,total,ionizing,dose,effects,bipolar,junction,transistors,methods,have,proven,powerful,various,research,fields,In,this,paper,show,that,could,benefit,from,such,present,machine,scientific,discovery,approach,TID,usually,cause,gain,degradation,leading,functional,failures,inte,grated,circuits,Currently,many,experiments,been,conducted,different,laboratories,worldwide,producing,large,amount,experimental,data,which,provides,wealth,information,However,difficult,utilize,these,effectively,study,proposed,new,artificial,neural,network,ANN,An,model,was,built,trained,using,collected,results,indicate,has,advantages,capturing,linear,correlations,predicting,suggests,hardness,tends,increase,current,possible,finding,analyzed,confirmed,through,irradiation
AB值：
0.545702
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