How the Analysis of Archival Data Could Provide Helpful Information About TID Degradation. Case Study: Bipolar Transistors

Pedro Martin-Holgado,Amor Romero-Maestre, Jose de-Martin-Hernandez,Jose J. Gonzalez-Lujan, Ivan Illera-Gomez, Yolanda Jimenez-de-Luna,Fernando Morilla, Mario Sacristan Barbero,Ruben Garcia Alia,Manuel Dominguez,Yolanda Morilla

IEEE Transactions on Nuclear Science(2022)

引用 2|浏览6
暂无评分
摘要
A critical step of radiation hardness assurance (RHA) for space systems is given by the parts selection in accordance with the observed (or estimated) radiation effects. Although radiation testing is the most decisive way of studying the radiation degradation of electronic components, the increasing use of commercial off-the-shelf (COTS) devices and the challenges posed by NewSpace are pushing the need of finding new approaches to assess the risk associated with radiation environments. This work tries to evaluate if valuable information might be extracted from archival data to carry out this assessment despite the well-known and dramatic lot-to-lot, or even part-to-part, variability for some technologies and the impact of the different test conditions, such as the bias conditions and the dose rate in enhanced low dose rate sensitivity (ELDRS). These factors are briefly analyzed for some examples. A new radiation database is briefly introduced, and some statistical approaches are cited, apart from the analysis herein followed. To finish, a first analysis on three families of bipolar transistors is presented together with the independent results from three external reports, with a good agreement between the experimental results and the expected ones.
更多
查看译文
关键词
Bipolar transistors,commercial off-the-shelf (COTS),data analysis,electrical,electronic,and electromechanical (EEE) parts database,gain degradation,lot-to-lot variability,machine learning (ML),NewSpace,part-to-part variability,PRECEDER,predictive analysis,radiation hardness assurance (RHA),radiation test,total ionizing dose (TID),virtual laboratory
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要